CYBERSECURITY DATASETS
Block Chain, DeFi and Smart Contracts Datasets:
The BCCC-SCsVuls-2024 dataset is a comprehensive resource for analyzing and detecting vulnerabilities in Solidity-based smart contracts, featuring 111,897 meticulously labeled samples across 11 vulnerabilities such as Re-entrancy (17,698), IntegerUO (16,740), DenialOfService (12,394), and Secure contracts (26,914). The dataset was curated from reputable sources like Smart Bugs, Ethereum SCs, and SmartScan-Dataset, ensuring diverse and representative vulnerability coverage. All entries were processed into SHA-256 hashes to maintain integrity and uniqueness, eliminating duplicates. This dataset provides a robust foundation for developing and testing vulnerability detection models for smart contracts, advancing research in blockchain security.
The full research paper outlining the details of the dataset and its underlying principles:
- Sepideh Hajihosseinkhani, Arash Habibi Lashkari, Ali Mizani, “Unveiling Smart Contracts Vulnerabilities: Toward Profiling Smart Contracts Vulnerabilities using Enhanced Genetic Algorithm and Generating Benchmark Dataset”, Blockchain: Research and Applications, December 2024, 100253
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Sepideh Hajihosseinkhani, Arash Habibi Lashkari, Ali Mizani, “Unveiling Smart Contracts Vulnerabilities: Toward Profiling Smart Contracts Vulnerabilities using Enhanced Genetic Algorithm and Generating Benchmark Dataset”, Blockchain: Research and Applications, December 2024, 100253
For more information and download this dataset, visit this page.
The BCCC-VulSCs-2023 dataset is a substantial collection for Solidity Smart Contracts (SCs) analysis, comprising 36,670 samples, each enriched with 70 feature columns. These features include the raw source code of the smart contract, a hashed version of the source code for secure referencing, and a binary label that indicates a contract as secure (0) or vulnerable (1). The dataset's extensive size and comprehensive features make it a valuable resource for machine-learning models to predict contract behavior, identify patterns, or classify contracts based on security and functionality criteria.
The full research paper outlining the details of the dataset and its underlying principles:
- Sepideh Hajihosseinkhani, Arash Habibi Lashkari, Ali Mizani, “Unveiling Vulnerable Smart Contracts: Toward Profiling Vulnerable Smart Contracts using Genetic Algorithm and Generating Benchmark Dataset”, Blockchain: Research and Applications, Vol. 4, 2023
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Sepideh Hajihosseinkhani, Arash Habibi Lashkari, Ali Mizani, “Unveiling Vulnerable Smart Contracts: Toward Profiling Vulnerable Smart Contracts using Genetic Algorithm and Generating Benchmark Dataset”, Blockchain: Research and Applications, Vol. 4, 2023
For more information and download this dataset, visit this page.
IoT Security Datasets:
The CIC-BCCC-NRC TabularIoTAttack-2024 dataset is a comprehensive collection of IoT network traffic data generated as part of an advanced effort to create a reliable source for training and testing AI-powered IoT cybersecurity models. This dataset is designed to address modern challenges in detecting and identifying IoT-specific cyberattacks, offering a rich and diverse set of labeled data that reflects realistic IoT network behaviours. The dataset extracted a wide array of network characteristics using CICFlowMeter, with each record containing relevant features such as network flows, timestamps, source/destination IPs, and attack labels.
The full research paper outlining the details of the dataset and its underlying principles:
- Tinshu Sasi, Arash Habibi Lashkari, Rongxing Lu, Pulei Xiong, Shahrear Iqbal, “An Efficient Self Attention-Based 1D-CNN-LSTM Network for IoT Attack Detection and Identification Using Network Traffic”, Journal of Information and Intelligence, 2024, ISSN 2949-7159, https://doi.org/10.1016/j.jiixd.2024.09.001
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Tinshu Sasi, Arash Habibi Lashkari, Rongxing Lu, Pulei Xiong, Shahrear Iqbal, “An Efficient Self Attention-Based 1D-CNN-LSTM Network for IoT Attack Detection and Identification Using Network Traffic”, Journal of Information and Intelligence, 2024, ISSN 2949-7159, https://doi.org/10.1016/j.jiixd.2024.09.001
For more information and download this dataset, visit this page.
Intrusion Detection (IDS/IPS) Datasets:
The BCCC-CSE-CIC-IDS2018 dataset is an enhanced version of CSE-CIC-IDS2018 with 46 million labelled records and 300 features, addressing key issues to improve data quality and reliability for behavioral profiling in IDS research. Labeling inconsistencies, particularly for DoS attacks, were corrected by aligning attack labels with attacker IPs instead of timestamps. NTLFlowLyzer, a new network traffic analyzer, was developed to resolve anomalies in extracted features and refine feature implementation. Additionally, protocol issues were fixed by removing UDP-based attacks previously misclassified due to TCP-specific analysis. Attacks with insufficient flow counts were retained but excluded from analysis and profiling. The dataset now includes an expanded feature set to better detect evolving cyber threats, making it a robust benchmark for AI-driven IDS/IPS research.
The full research paper outlining the details of the dataset and its underlying principles:
- MohammadMoein Shafi, Arash Habibi Lashkari, Arousha Haghighian Roudsari, "Toward Generating a Large Scale Intrusion Detection Dataset and Intruders Behavioral Profiling Using Network and Transportation Layers Traffic Flow Analyzer (NTLFlowLyzer)", Journal of Network and Systems Management, Vol 33, article 44, 2025
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- MohammadMoein Shafi, Arash Habibi Lashkari, Arousha Haghighian Roudsari, "Toward Generating a Large Scale Intrusion Detection Dataset and Intruders Behavioral Profiling Using Network and Transportation Layers Traffic Flow Analyzer (NTLFlowLyzer)", Journal of Network and Systems Management, Vol 33, article 44, 2025
For more information and download this dataset, visit this page.
Using NLFlowLyzer, we successfully generated the “BCCC-CIC-IDS2017” dataset by extracting key flows from raw network traffic data of CIC-IDS2017, resulting in CSV files integrating essential network and transport layer features. This new dataset offers a structured approach for analyzing intrusion detection, combining diverse traffic types into multiple sub-categories. The “BCCC-CIC-IDS2017” dataset enriches the depth and variety needed to rigorously evaluate our proposed profiling model, advancing research in network security and enhancing the development of intrusion detection systems.
The full research paper outlining the details of the dataset and its underlying principles:
- MohammadMoein Shafi, Arash Habibi Lashkari, Arousha Haghighian Roudsari, "NTLFlowLyzer: Toward Generating an Intrusion Detection Dataset and Intruders Behavior Profiling through Network Layer Traffic Analysis and Pattern Extraction", Computer & Security, Computers & Security, 104160, ISSN 0167-4048
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- MohammadMoein Shafi, Arash Habibi Lashkari, Arousha Haghighian Roudsari, "NTLFlowLyzer: Toward Generating an Intrusion Detection Dataset and Intruders Behavior Profiling through Network Layer Traffic Analysis and Pattern Extraction", Computer & Security, Computers & Security, 104160, ISSN 0167-4048
For more information and download this dataset, visit this page.
The distributed denial of service attack poses a significant threat to network security. The effectiveness of new detection methods depends heavily on well-constructed datasets. After conducting an in-depth analysis of 16 publicly available datasets and identifying their shortcomings across various dimensions, the 'BCCC-cPacket-Cloud-DDoS-2024' is meticulously created, addressing challenges identified in previous datasets through a cloud infrastructure. The dataset contains over eight benign user activities and 17 DDoS attack scenarios. The dataset is fully labeled (with a total of 26 labels) with over 300 features extracted from the network and transport layers of the traffic flows using NTLFlowLyzer. The dataset's extensive size and comprehensive features make it a valuable resource for researchers and practitioners to develop and validate more robust and accurate DDoS detection and mitigation strategies. Furthermore, researchers can leverage the 'BCCC-cPacket-Cloud-DDoS-2024' dataset to train learning-based models aimed at predicting benign user behavior, detecting attacks, identifying patterns, classifying network data, etc.
The full research paper outlining the details of the dataset and its underlying principles:
- Shafi, MohammadMoein, Arash Habibi Lashkari, Vicente Rodriguez, and Ron Nevo.; "Toward Generating a New Cloud-Based Distributed Denial of Service (DDoS) Dataset and Cloud Intrusion Traffic Characterization", Information, Vol. 15, no. 4: 195
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Shafi, MohammadMoein, Arash Habibi Lashkari, Vicente Rodriguez, and Ron Nevo.; "Toward Generating a New Cloud-Based Distributed Denial of Service (DDoS) Dataset and Cloud Intrusion Traffic Characterization", Information, Vol. 15, no. 4: 195
For more information and download this dataset, visit this page.
To generate the CIC-UNSW-NB15 we used CICFlowMeter to extract the new set of features from the provided captured network traffic data by the UNSW-NB15. After extracting the flows using CICFlowMeter, we need to label them using the ground truth from the original dataset files. We matched the extracted flows with the records in the ground truth file based on the source IP, destination IP, source port, destination port, and protocol.
If any flows match with a record from the ground truth file, we set the label using the ground truth attack category. If the flow is matched with more than one record from the ground truth file, we compare the timestamps and choose the record's label that matches the flow timestamp.
In the worst case, the flow will be dropped even if we cannot decide on the label by comparing the timestamp. Any remaining flows will be labeled benign after labeling all the malicious flows..
The full research paper outlining the details of the dataset and its underlying principles:
- H. Mohammadian, A. H. Lashkari, A. Ghorbani. “Poisoning and Evasion: Deep Learning-Based NIDS under Adversarial Attacks,” 21st Annual International Conference on Privacy, Security and Trust (PST), 2024.
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- H. Mohammadian, A. H. Lashkari, A. Ghorbani. “Poisoning and Evasion: Deep Learning-Based NIDS under Adversarial Attacks,” 21st Annual International Conference on Privacy, Security and Trust (PST), 2024.
For more information and download this dataset, visit this page.
The final dataset includes 12 DDoS attack NTP, DNS, LDAP, MSSQL, NetBIOS, SNMP, SSDP, UDP, UDP-Lag, WebDDoS, SYN and TFTP in the training day and 7 attacks
including PortScan, NetBIOS, LDAP, MSSQL, UDP, UDP-Lag and SYN in the testing day (CAPEC standard). The infrastructure includes Third-Party for the attack side and the victim
organization has 4 machines and 1 server. The dataset includes the captures network traffic along with 80 features extracted from the captured traffic
using CICFlowmeter-V3.0.
The full research paper outlining the details of the dataset and its underlying principles:
- Iman Sharafaldin, Arash Habibi Lashkari, Saqib Hakak, and Ali A. Ghorbani, "Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy", IEEE 53rd International Carnahan Conference on Security Technology, Chennai, India, 2019
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Iman Sharafaldin, Arash Habibi Lashkari, Saqib Hakak, and Ali A. Ghorbani, "Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy", IEEE 53rd International Carnahan Conference on Security Technology, Chennai, India, 2019
For more information and download this dataset, visit this page.
The final dataset includes seven different attack scenarios: Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and infiltration of the network from inside (CAPEC standard).
The attacking infrastructure includes 50 machines and the victim organization has 5 departments and includes 420 machines and 30 servers.
The dataset includes the captures network traffic and system logs of each machine, along with 80 features extracted from the captured traffic
using CICFlowmeter-V3.0.
The full research papers outlining the details of the dataset and its underlying principles:
- Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization", 4th International Conference on Information Systems Security and Privacy (ICISSP), Purtogal, January 2018.
- Gurdip Kaur, Arash Habibi Lashkari and Abir Rahali, "Intrusion Traffic Detection and Characterization using Deep Image Learning", The 5th Cyber Science and Technology Congress (2020) (CyberSciTech 2020), Vancouver, Canada, August 2020.
For more information and download this dataset, visit this page.
The full research papers outlining the details of the dataset and its underlying principles:
- Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization", 4th International Conference on Information Systems Security and Privacy (ICISSP), Purtogal, January 2018.
- Gurdip Kaur, Arash Habibi Lashkari and Abir Rahali, "Intrusion Traffic Detection and Characterization using Deep Image Learning", The 5th Cyber Science and Technology Congress (2020) (CyberSciTech 2020), Vancouver, Canada, August 2020.
For more information and download this dataset, visit this page.
Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are the most important defense tools against the sophisticated and ever-growing
network attacks. Due to the lack of reliable test and validation datasets, anomaly-based intrusion detection approaches are suffering from consistent and
accurate performance evolutions. Our evaluations of the existing eleven datasets since 1998 show that most are out of date and unreliable to use. Some of
these datasets suffer from the lack of traffic diversity and volumes, some do not cover the variety of known attacks, while others anonymize packet payload data,
which cannot reflect the current trends. Some are also lacking feature set and metadata. CICIDS2017 dataset contains benign and the most up-to-date common attacks (The 2016 McAfee report and CAPEC standard),
which resembles the true real-world data (PCAPs). It also includes the results of the network traffic analysis using
CICFlowmeter-V3.0 with labeled flows based on the time stamp, source and destination IPs,
source and destination ports, protocols and attack (CSV files).
The full research papers outlining the details of the dataset and its underlying principles:
- Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization", 4th International Conference on Information Systems Security and Privacy (ICISSP), Purtogal, January 2018.
- Gurdip Kaur, Arash Habibi Lashkari and Abir Rahali, "Intrusion Traffic Detection and Characterization using Deep Image Learning", The 5th Cyber Science and Technology Congress (2020) (CyberSciTech 2020), Vancouver, Canada, August 2020.
For more information and download the dataset, visit this page.
The full research papers outlining the details of the dataset and its underlying principles:
- Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization", 4th International Conference on Information Systems Security and Privacy (ICISSP), Purtogal, January 2018.
- Gurdip Kaur, Arash Habibi Lashkari and Abir Rahali, "Intrusion Traffic Detection and Characterization using Deep Image Learning", The 5th Cyber Science and Technology Congress (2020) (CyberSciTech 2020), Vancouver, Canada, August 2020.
For more information and download the dataset, visit this page.
Malware Datasets:
The BCCC-Mal-NetMem-2025 dataset comprises over 7.7 million labeled records generated from controlled experiments involving 15 malware categories and 32 individual malware samples. These categories include ransomware, Trojan downloaders, coin miners, remote access tools (RATs), spyware, backdoors, and worms. The data was collected by executing each malware in isolated Windows environments equipped with real-time network and memory monitoring tools to ensure comprehensive behavioral capture. The dataset integrates both memory and network traffic features, offering a multidimensional view of malware behavior for accurate profiling. This hybrid structure allows for advanced AI-driven threat detection and malware characterization, with consistent labeling and session-based organization that supports detailed analysis. The BCCC-Mal-NetMem-2025 is a unique benchmark for behavioral malware analysis, bridging gaps between static profiling and real-world execution patterns.
The full research paper outlining the details of the dataset and its underlying principles:
- Arash Habibi Lashkari, MohammadMoein Shafi, Yongkun Li, Abhay Pratap Singh, Ashley Barkworth, "Unveiling Evasive Malware Behavior: Towards Generating a Multi-Sources Benchmark Dataset and Evasive Malware Behavior Profiling Using Network Traffic and Memory Analysis", Journal of Supercomputing, 2025
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Arash Habibi Lashkari, MohammadMoein Shafi, Yongkun Li, Abhay Pratap Singh, Ashley Barkworth, "Unveiling Evasive Malware Behavior: Towards Generating a Multi-Sources Benchmark Dataset and Evasive Malware Behavior Profiling Using Network Traffic and Memory Analysis", Journal of Supercomputing, 2025
For more information and download this dataset, visit this page.
The obfuscated malware dataset is designed to test obfuscated malware detection methods through memory. In this research, we present a new malware memory analysis dataset (MalMem-2022), which was created to represent as close to a real-world situation as possible using malware prevalent in the real world and consists of 58,596 records with 29,298 benign and 29,298 malicious.
The full research paper outlining the details of the dataset and its underlying principles:
- Tristan Carrier, Princy Victor, Ali Tekeoglu, Arash Habibi Lashkari,” Detecting Obfuscated Malware using Memory Feature Engineering”, The 8th International Conference on Information Systems Security and Privacy (ICISSP), 2022
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Tristan Carrier, Princy Victor, Ali Tekeoglu, Arash Habibi Lashkari,” Detecting Obfuscated Malware using Memory Feature Engineering”, The 8th International Conference on Information Systems Security and Privacy (ICISSP), 2022
For more information and download this dataset, visit this page.
In this research, we present a new evasive pdf dataset, Evasive-PDFMal2022 which consists of 10,025 records with 5557 malicious and 4468 benign records that tend to evade the common significant features found in each class. This makes them harder to detect by common learning algorithms.
The full research paper outlining the details of the dataset and its underlying principles:
- Maryam Issakhani, Princy Victor, Ali Tekeoglu, and Arash Habibi Lashkari1, “PDF Malware Detection Based on Stacking Learning”, The International Conference on Information Systems Security and Privacy, February 2022,
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Maryam Issakhani, Princy Victor, Ali Tekeoglu, and Arash Habibi Lashkari1, “PDF Malware Detection Based on Stacking Learning”, The International Conference on Information Systems Security and Privacy, February 2022,
For more information and download this dataset, visit this page.
This research work proposes a new comprehensive and huge android malware dataset, named CCCS-CIC-AndMal-2020. The dataset includes 200K benign and 200K malware samples totalling to 400K android apps with 14 prominent malware categories and 191 eminent malware families. To generate the representative dataset, we collaborated with CCCS to capture 200K android malware apps which are labeled and characterized into corresponding family. Benign android apps (200K) are collected from Androzoo dataset to balance the huge dataset. We collected 14 malware categories including adware, backdoor, file infector, no category, Potentially Unwanted Apps (PUA), ransomware, riskware, scareware, trojan, trojan-banker, trojan-dropper, trojan-sms, trojan-spy and zero-day. A complete taxonomy of all the malware families of captured malware apps is created by dividing them into eight categories such as sensitive data collection, media, hardware, actions/activities, internet connection, C&C, antivirus and storage & settings.
The full research paper outlining the details of the dataset and its underlying principles:
- Abir Rahali, Arash Habibi Lashkari, Gurdip Kaur, Laya Taheri, Francois Gagnon, and Frédéric Massicotte, “DIDroid: Android Malware Classification and Characterization Using Deep Image Learning”, 10th International Conference on Communication and Network Security, Tokyo, Japan, November 2020
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Abir Rahali, Arash Habibi Lashkari, Gurdip Kaur, Laya Taheri, Francois Gagnon, and Frédéric Massicotte, “DIDroid: Android Malware Classification and Characterization Using Deep Image Learning”, 10th International Conference on Communication and Network Security, Tokyo, Japan, November 2020
For more information and download this dataset, visit this page.
We provide the second part of the CICAndMal2017 dataset publicly available which includes permissions and intents as static features and API calls and all generated log files as dynamic features in three steps (During installation, before restarting and after restarting the phone). In this part, we improve our malware category and family classification performance around 30% by combining the previous dynamic features (80 network-flows by using CICFlowmeter-V3.0) with 2-gram sequential relations of API calls. In addition, we examine these features in the presented two-layer malware analysis framework. Besides these, we provide other captured features such as battery states, log states, packages, process logs, etc.
The full research paper outlining the details of the dataset and its underlying principles:
- Laya Taheri, Andi Fitriah Abdulkadir, Arash Habibi Lashkari, "Extensible Android Malware Detection and Family Classification Using Network-Flows and API-Calls", The IEEE (53rd) International Carnahan Conference on Security Technology, India, 2019
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Laya Taheri, Andi Fitriah Abdulkadir, Arash Habibi Lashkari, "Extensible Android Malware Detection and Family Classification Using Network-Flows and API-Calls", The IEEE (53rd) International Carnahan Conference on Security Technology, India, 2019
For more information and download this dataset, visit this page.
We propose our new Android malware dataset here, named CICAndMal2017.
In this approach, we run our both malware and benign applications on real smartphones to avoid runtime behavior modification of advanced malware samples
that are able to detect the emulator environment. We collected more than 10,854 samples (4,354 malware and 6,500 benign) from several sources.
We have collected over six thoUnited States of Americand benign apps from Googleplay market published in 2015, 2016, 2017. In this dataset, we installed 5,000 of the collected
samples (426 malware and 5,065 benign) on real devices. Our malware samples in the CICAndMal2017 dataset are classified into four categories Adware, Ransomware, Scareware and SMS Malware.
Our samples come from 42 unique malware families.
The full research paper outlining the details of the dataset and its underlying principles:
- Arash Habibi Lashkari, Andi Fitriah A.Kadir, Laya Taheri, and Ali A. Ghorbani, “Toward Developing a Systematic Approach to Generate Benchmark Android Malware Datasets and Classification”, In the proceedings of the 52nd IEEE International Carnahan Conference on Security Technology (ICCST), Montreal, Quebec, Canada, 2018.
For more information and download the dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Arash Habibi Lashkari, Andi Fitriah A.Kadir, Laya Taheri, and Ali A. Ghorbani, “Toward Developing a Systematic Approach to Generate Benchmark Android Malware Datasets and Classification”, In the proceedings of the 52nd IEEE International Carnahan Conference on Security Technology (ICCST), Montreal, Quebec, Canada, 2018.
For more information and download the dataset, visit this page.
The sophisticated and advanced Android malware is able to identify the presence of the emulator used by the malware analyst and in response, alter its behavior to evade detection.
To overcome this issue, we installed the Android applications on the real device and captured its network traffic. AAGM dataset is captured by installing the Android apps on the real smartphones semi-automated. The dataset is generated from 1900 applications with the following three categories:
- Android Adware (250 apps): Airpush, Dowgin, Kemoge, Mobidash, Shuanet
- General Android Malware (150 apps): AVpass, FakeAV, FakeFlash/FakePlayer, GGtracker, Penetho
- Benign (1500 apps): 2015 and 2016 GooglePlay market (top free popular and top free new).
The full research papers outlining the details of the dataset and its underlying principles:
- Arash Habibi Lashkari, Andi Fitriah A.Kadir, Hugo Gonzalez, Kenneth Fon Mbah and Ali A. Ghorbani, “Towards a Network-Based Framework for Android Malware Detection and Characterization”, In the proceeding of the 15th International Conference on Privacy, Security and Trust, PST, Calgary, Canada, 2017.
For more information and download the dataset, visit this page.
- Android Adware (250 apps): Airpush, Dowgin, Kemoge, Mobidash, Shuanet
- General Android Malware (150 apps): AVpass, FakeAV, FakeFlash/FakePlayer, GGtracker, Penetho
- Benign (1500 apps): 2015 and 2016 GooglePlay market (top free popular and top free new).
The full research papers outlining the details of the dataset and its underlying principles:
- Arash Habibi Lashkari, Andi Fitriah A.Kadir, Hugo Gonzalez, Kenneth Fon Mbah and Ali A. Ghorbani, “Towards a Network-Based Framework for Android Malware Detection and Characterization”, In the proceeding of the 15th International Conference on Privacy, Security and Trust, PST, Calgary, Canada, 2017.
For more information and download the dataset, visit this page.
DNS Security Datasets:
Using ALFlowLyzer, we successfully generated an augmented dataset, "BCCC-CIC-Bell-DNS-2024," from two existing datasets: "CIC-Bell-DNS-2021" and "CIC-Bell-DNS-EXF-2021." ALFlowLyzer enabled the extraction of essential flows from raw network traffic data, resulting in CSV files that integrate DNS metadata and application layer features. This new dataset combines light and heavy data exfiltration traffic into six unique sub-categories, providing a comprehensive structure for analyzing DNS data exfiltration attacks. The "BCCC-CIC-Bell-DNS-2024" dataset enhances the richness and diversity needed to evaluate our proposed profiling model effectively.
The full research paper outlining the details of the dataset and its underlying principles:
- MohammadMoein Shafi, Arash Habibi Lashkari, Hardhik Mohanty; "Unveiling Malicious DNS Behavior Profiling and Generating Benchmark Dataset through Application Layer Traffic Analysis", Computers and Electrical Engineering, Vol 118, P B, Sep. 2024
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- MohammadMoein Shafi, Arash Habibi Lashkari, Hardhik Mohanty; "Unveiling Malicious DNS Behavior Profiling and Generating Benchmark Dataset through Application Layer Traffic Analysis", Computers and Electrical Engineering, Vol 118, P B, Sep. 2024
For more information and download this dataset, visit this page.
DoHBrw-2020' dataset, which is skewed with about 90% malicious and only 10% benign Domain over HTTPS (DoH) network traffic, the 'BCCC-CIRA-CIC-DoHBrw-2020' dataset offers a more balanced composition. It includes equal numbers of malicious and benign DoH network traffic instances, with 249,836 instances in each category. This balance was achieved using the Synthetic Minority Over-sampling Technique (SMOTE).Â
The full research paper outlining the details of the dataset and its underlying principles:
- Sepideh Niktabe, Arash Habibi Lashkari, Arousha Haghighian Roudsari, “Unveiling DoH Tunnel: Toward Generating a Balanced DoH EncryptedTraffic Dataset and Profiling malicious Behaviour using InherentlyInterpretable Machine Learning“, Peer-to-Peer Networking and Applications, Vol. 17, 2023
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Sepideh Niktabe, Arash Habibi Lashkari, Arousha Haghighian Roudsari, “Unveiling DoH Tunnel: Toward Generating a Balanced DoH EncryptedTraffic Dataset and Profiling malicious Behaviour using InherentlyInterpretable Machine Learning“, Peer-to-Peer Networking and Applications, Vol. 17, 2023
For more information and download this dataset, visit this page.
In this research work, we are releasing CIC-Bell-DNS-EXF-2021, a large dataset of 270.8 MB DNS traffic generated by exfiltrating various file types ranging from small to large sizes.
We leverage our developed feature extractor to extract 30 features from the DNS packets, resulting in a final structured dataset of 323,698 heavy attack samples (CAPEC standard), 53,978 light attack samples, and 641,642 distinct benign samples.
The experimental analysis of utilizing several Machine Learning (ML) algorithms on our dataset shows the effectiveness of our hybrid detection system even in the existence of light DNS traffic.
The full research paper outlining the details of the dataset and its underlying principles:
- Samaneh Mahdavifar, Amgad Hanafy Salem, Princy Victor, Miguel Garzon, Amir H. Razavi, Natasha Hellberg, Arash Habibi Lashkari, “Lightweight Hybrid Data Exfiltration using DNS based on Machine Learning”, The 11th IEEE International Conference on Communication and Network Security (ICCNS), Dec. 3-5, 2021, Beijing Jiaotong University, Weihai, China.
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Samaneh Mahdavifar, Amgad Hanafy Salem, Princy Victor, Miguel Garzon, Amir H. Razavi, Natasha Hellberg, Arash Habibi Lashkari, “Lightweight Hybrid Data Exfiltration using DNS based on Machine Learning”, The 11th IEEE International Conference on Communication and Network Security (ICCNS), Dec. 3-5, 2021, Beijing Jiaotong University, Weihai, China.
For more information and download this dataset, visit this page.
In this research work, we generate and release a large DNS features dataset of 400,000 benign and 13,011 malicious samples processed from a million benign and 51,453 known-malicious domains from publicly available datasets. The malicious samples span between three categories of spam, phishing, and malware. Our dataset, namely CIC-Bell-DNS2021 replicates the real-world scenarios with frequent benign traffic and diverse malicious domain types. We train and validate a classification model that, unlike previous works that focus on binary detection, detects the type of the attack, i.e., spam, phishing, and malware. Classification performance of various ML models on our generated dataset proves the effectiveness of our model, with the highest, is k-Nearest Neighbors (k-NN) achieving 94.8% and 99.4% F1-Score for balanced data ratio (60/40%) and imbalanced data ratio (97/3%), respectively. Finally, we have gone through feature evaluation using information gain analysis to get the merits of each feature in each category, proving the third party features as the most influential one among the top 13 features.
The full research paper outlining the details of the dataset and its underlying principles:
- Samaneh Mahdavifar, Nasim Maleki, Arash Habibi Lashkari, Matt Broda, Amir H. Razavi, “Classifying Malicious Domains using DNS Traffic Analysis”, The 19th IEEE International Conference on Dependable, Autonomic, and Secure Computing (DASC), Oct. 25-28, 2021, Calgary, Canada
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Samaneh Mahdavifar, Nasim Maleki, Arash Habibi Lashkari, Matt Broda, Amir H. Razavi, “Classifying Malicious Domains using DNS Traffic Analysis”, The 19th IEEE International Conference on Dependable, Autonomic, and Secure Computing (DASC), Oct. 25-28, 2021, Calgary, Canada
For more information and download this dataset, visit this page.
This research work proposes a systematic approach to generate a typical dataset to analyze, test, and evaluate DoH traffic in covert channels and tunnels. The main objective of this project is to deploy DoH within an application and capture benign as well as malicious DoH traffic as a two-layered approach to detect and characterize DoH traffic using time-series classifier. The final dataset includes implementing DoH protocol within an application using five different browsers and tools and four servers to capture Benign-DoH, Malicious-DoH and non-DoH traffic. Layer 1 of the proposed two-layered approach is used to classify DoH traffic from non-DoH traffic and layer 2 is used to characterize Benign-Doh from Malicious-DoH traffic. The browsers and tools used to capture traffic include Google Chrome, Mozilla Firefox, dns2tcp, DNSCat2, and Iodine while the servers used to respond to DoH requests are AdGuard, Cloudflare, Google DNS, and Quad9. We developed a traffic analyzer namely DoHLyzer to extrac features from the captured traffic.
The full research paper outlining the details of the dataset and its underlying principles:
- Mohammadreza MontazeriShatoori, Logan Davidson, Gurdip Kaur and Arash Habibi Lashkari, "Detection of DoH Tunnels using Time-series Classification of Encrypted Traffic", The 5th Cyber Science and Technology Congress (2020) (CyberSciTech 2020), Vancouver, Canada, August 2020
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Mohammadreza MontazeriShatoori, Logan Davidson, Gurdip Kaur and Arash Habibi Lashkari, "Detection of DoH Tunnels using Time-series Classification of Encrypted Traffic", The 5th Cyber Science and Technology Congress (2020) (CyberSciTech 2020), Vancouver, Canada, August 2020
For more information and download this dataset, visit this page.
The Web has long become a major platform for online criminal activities. URLs are used as the main vehicle in this domain. To counter this issues security community focused its efforts on developing techniques for mostly blacklisting of malicious URLs. While successful in protecting users from known malicious domains, this approach only solves part of the problem. The new malicious URLs that sprang up all over the web in masses commonly get a head start in this race. Besides that, Alexa ranked, trusted websites may convey compromised fraudulent URLs called defacement URL. We explore a lightweight approach to detection and categorization of the malicious URLs according to their attack type and show that lexical analysis is effective and efficient for proactive detection of these URLs. We also study the effect of the obfuscation techniques on malicious URLs to figure out the type of obfuscation technique targeted at specific type of malicious URL. We study mainly five different types of URLs include Benign, Spam, Phishing, Malware, and Defacement.
The full research paper outlining the details of the dataset and its underlying principles:
- Mohammad Saiful Islam Mamun, Mohammad Ahmad Rathore, Arash Habibi Lashkari, Natalia Stakhanova and Ali A. Ghorbani,"Detecting Malicious URLs Using Lexical Analysis", Network and System Security, Springer International Publishing, P467--482, 2016.
For more information and download the dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Mohammad Saiful Islam Mamun, Mohammad Ahmad Rathore, Arash Habibi Lashkari, Natalia Stakhanova and Ali A. Ghorbani,"Detecting Malicious URLs Using Lexical Analysis", Network and System Security, Springer International Publishing, P467--482, 2016.
For more information and download the dataset, visit this page.
Dark Web and Dark Net Datasets:
BCCC-DarkNet-2025 is an augmented, research-driven dataset that supports encrypted traffic analysis and threat detection across anonymized communication networks. It integrates and extends two benchmark datasets, CIC-Darknet2020 and Darknet-Dataset-2020, selected for their robust coverage of encryption protocols and darknet-specific traffic behaviors. The dataset includes diverse encrypted traffic types like VPN, Tor, I2P, Freenet, and ZeroNet, with multi-class labeling and protocol-specific annotations. These sources were chosen based on well-defined criteria, including support for time-dependent patterns, entropy measures, and behavioral features critical to identifying obfuscated malicious activities. The dataset has been standardized to ensure consistency and scalability using NTLFlowLyzer-V3, a custom flow-based feature extractor. This preprocessing step harmonizes temporal, statistical, and protocol-level features across both datasets, enabling seamless integration into machine learning pipelines. The result is BCCC-DarkNet-2025, a unified and enriched dataset that significantly improves classifier performance in encrypted traffic scenarios by capturing structural and behavioral anomalies. It is a valuable resource for developing AI-powered cybersecurity solutions in dynamic and evasive threat environments.
The full research paper outlining the details of the dataset and its underlying principles:
- Adit Sharma and Arash Habibi Lashkari, “Hybrid attention-enhanced explainable model for encrypted traffic detection and classification”, International Journal of Information Security, Volume 24, article number 144, 2025
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Adit Sharma and Arash Habibi Lashkari, “Hybrid attention-enhanced explainable model for encrypted traffic detection and classification”, International Journal of Information Security, Volume 24, article number 144, 2025
For more information and download this dataset, visit this page.
This research work proposes a novel technique to detect and characterize VPN and Tor applications together as the real representative of darknet traffic by amalgamating out two public datasets, namely, ISCXTor2016 and ISCXVPN2016, to create a complete darknet dataset covering Tor and VPN traffic respectively..
The full research paper outlining the details of the dataset and its underlying principles:
- Arash Habibi Lashkari, Gurdip Kaur, and Abir Rahali, “DIDarknet: A Contemporary Approach to Detect and Characterize the Darknet Traffic using Deep Image Learning”, 10th International Conference on Communication and Network Security, Tokyo, Japan, November 2020
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Arash Habibi Lashkari, Gurdip Kaur, and Abir Rahali, “DIDarknet: A Contemporary Approach to Detect and Characterize the Darknet Traffic using Deep Image Learning”, 10th International Conference on Communication and Network Security, Tokyo, Japan, November 2020
For more information and download this dataset, visit this page.
To be sure about the quantity and diversity of this dataset, we defined a set of tasks to generate a representative dataset of real-world traffic. We created three
users for the browser traffic collection and two users for the communication parts such as chat, mail, FTP, p2p, etc. For the non-Tor traffic we used previous benign traffic
from VPN project and for the Tor traffic we used 7 traffic categories: Browsing, Email, Chat, Audio-Streaming, Video-Streaming, FTP, VoIP, P2P. The traffic was captured using
Wireshark and tcpdump, generating a total of 22GB of data. To facilitate the labeling process, as we explained in the related published paper, we captured the outgoing traffic
at the workstation and the gateway simultaneously, collecting a set of pairs of .pcap files: one regular traffic pcap (workstation) and one Tor traffic pcap (gateway) file. Later,
we labelled the captured traffic in two steps. First, we processed the .pcap files captured at the workstation: we extracted the flows, and we confirmed that the majority of traffic
flows were generated by application X (Skype, ftps, etc.), the object of the traffic capture. Then, we labelled all flows from the Tor .pcap file
as X. ISCXFlowMeter has been written in Java for reading the pcap files and create the csv file based on
selected features. The dataset consists of labeled network traffic, including full packet in pcap format and csv (flows generated by CICFlowMeter) also are publicly available
for researchers.
The full research paper outlining the details of the dataset and its underlying principles:
- Arash Habibi Lashkari, Gerard Draper-Gil, Mohammad Saiful Islam Mamun and Ali A. Ghorbani, "Characterization of Tor Traffic Using Time Based Features", In the proceeding of the 3rd International Conference on Information System Security and Privacy, SCITEPRESS, Porto, Portugal, 2017.
For more information and download the dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Arash Habibi Lashkari, Gerard Draper-Gil, Mohammad Saiful Islam Mamun and Ali A. Ghorbani, "Characterization of Tor Traffic Using Time Based Features", In the proceeding of the 3rd International Conference on Information System Security and Privacy, SCITEPRESS, Porto, Portugal, 2017.
For more information and download the dataset, visit this page.
To generate a representative dataset of real-world traffic, we defined a set of tasks, assuring that our dataset is rich enough in diversity and quantity. We created
accounts for users Alice and Bob in order to use services like Skype, Facebook, etc. Below we provide the complete list of different types of traffic and applications considered
in our dataset for each traffic type (VoIP, P2P, etc.). We captured a regular session and a session over VPN, therefore we have a total of 14 traffic categories: VOIP, VPN-VOIP,
P2P, VPN-P2P, etc. We also give a detailed description of the different types of traffic generated: Browsing, Email, Chat, Audio-Streaming, Video-Streaming, FTP, VoIP, P2P.
The traffic was captured using Wireshark and tcpdump, generating a total amount of 28GB of data. For the VPN, we used an external VPN service provider and connected to it
using OpenVPN (UDP mode). To generate SFTP and FTPS traffic we also used an external service provider and Filezilla as a client. To facilitate the labeling process, when capturing
the traffic all unnecessary services and applications were closed. (The only application executed was the objective of the capture, e.g., Skype voice-call, SFTP file transfer, etc.)
We used a filter to capture only the packets with source or destination IP, the address of the local client (Alice or Bob).
ISCXFlowMeter (formerly known as ISCXFlowMeter) has been written in Java for reading the pcap files and create the csv file based on selected features. The dataset consists of labeled network traffic, including full packet in pcap format and csv (flows generated by CICFlowMeter) also are publicly available for researchers.
The full research paper outlining the details of the dataset and its underlying principles:
- Gerard Drapper Gil, Arash Habibi Lashkari, Mohammad Mamun, Ali A. Ghorbani, "Characterization of Encrypted and VPN Traffic Using Time-Related Features", In Proceedings of the 2nd International Conference on Information Systems Security and Privacy(ICISSP 2016), pages 407-414, Rome, Italy, 2016.
For more information and download the dataset, visit this page.
ISCXFlowMeter (formerly known as ISCXFlowMeter) has been written in Java for reading the pcap files and create the csv file based on selected features. The dataset consists of labeled network traffic, including full packet in pcap format and csv (flows generated by CICFlowMeter) also are publicly available for researchers.
The full research paper outlining the details of the dataset and its underlying principles:
- Gerard Drapper Gil, Arash Habibi Lashkari, Mohammad Mamun, Ali A. Ghorbani, "Characterization of Encrypted and VPN Traffic Using Time-Related Features", In Proceedings of the 2nd International Conference on Information Systems Security and Privacy(ICISSP 2016), pages 407-414, Rome, Italy, 2016.
For more information and download the dataset, visit this page.
Operational Technology Datasets:
The CIC Modbus Dataset contains network (pcap) captures and attack logs from a simulated substation network. The dataset is categorized into two groups: an attack dataset and a benign dataset. The attack dataset includes network traffic captures that simulate various types of Modbus protocol attacks in a substation environment. The attacks are reconnaissance, query flooding, loading payloads, delay response, modify length parameters, false data injection, stacking Modbus frames, brute force write and baseline replay. These attacks are based of some techniques in the MITRE ICS ATT&CK framework. On the other hand, the benign dataset consists of normal network traffic captures representing legitimate Modbus communication within the substation network.The purpose of this dataset is to facilitate research, analysis, and development of intrusion detection systems, anomaly detection algorithms and other security mechanisms for substation networks using the Modbus protocol.
The full research paper outlining the details of the dataset and its underlying principles:
- Kwasi Boakye-Boateng, Ali A. Ghorbani, and Arash Habibi Lashkari, " Securing Substations with Trust, Risk Posture and Multi-Agent Systems: A Comprehensive Approach,"20th International Conference on Privacy, Security and Trust (PST), Copenhagen, Denmark, August. 2023
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Kwasi Boakye-Boateng, Ali A. Ghorbani, and Arash Habibi Lashkari, " Securing Substations with Trust, Risk Posture and Multi-Agent Systems: A Comprehensive Approach,"20th International Conference on Privacy, Security and Trust (PST), Copenhagen, Denmark, August. 2023
For more information and download this dataset, visit this page.
Software and Source Code Datasets:
This dataset consists of a collection of 11,012 evasive or sophisticated malicious SQL queries. These queries are generated using a genetic algorithm applied to the Kaggle malicious SQL dataset. The goal of the genetic algorithm is to enhance the evasiveness and sophistication of the original malicious queries.
The full research paper outlining the details of the dataset and its underlying principles:
- Maryam Issakhani, Mufeng Huang, Mohammad A. Tayebi, Arash Habibi Lashkari, "An Evolutionary Algorithm for Adversarial SQL Injection Attack Generation", IEEE Intelligence and Security Informatics (ISI2023), NC, United States of America
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Maryam Issakhani, Mufeng Huang, Mohammad A. Tayebi, Arash Habibi Lashkari, "An Evolutionary Algorithm for Adversarial SQL Injection Attack Generation", IEEE Intelligence and Security Informatics (ISI2023), NC, United States of America
For more information and download this dataset, visit this page.
Source Code Authorship Attribution (SCAA) is the technique to find the real author of source code in a corpus.
Though it is a privacy threat to open-source programmers, ithas shown to be significantly helpful in developing forensic-based applications such as ghostwriting detection, copyright dispute settlements, catching authors of malicious applications using source code, and other code analysis applications.
This dataset was created by extracting ’code’ data from the GCJ, and Github datasets, including examples of attacks and adversarial examples, were created using Source Code imitator. The dataset in a total of 19,000 code files from 300 authors.
The full research paper outlining the details of the dataset and its underlying principles:
- Abhishek Chopra , Nikhill Vombatkere , Arash Habibi Lashkari,”AuthAttLyzer: A Robust defensive distillation-based Authorship Attribution framework”, The 12th International Conference on Communication and Network Security (ICCNS), 2022, China
For more information and download this dataset, visit this page.
The full research paper outlining the details of the dataset and its underlying principles:
- Abhishek Chopra , Nikhill Vombatkere , Arash Habibi Lashkari,”AuthAttLyzer: A Robust defensive distillation-based Authorship Attribution framework”, The 12th International Conference on Communication and Network Security (ICCNS), 2022, China
For more information and download this dataset, visit this page.
Download Requests: Companies, research centres and universities that have downloaded these datasets, 250K+ since 2015:
A
A&M College, United States of America Aalborg University, Denmark  Aalim muhammed salegh college of engineering, India Aalto university, Finland Abbottabad University Of Science & Technology (AUST), Pakistan Abdullah Gul University (AGU), Turkey Abiola Ajimobi Technical University, Ibadan, Nigeria Abudhabi University, United Arab Emirates Academia de Studii Economice din Bucuresti (ASE), Romania Academic Student Computer Engineer, Libyan Academy, Libya Association for Computing Machinery (ACM), China Acharya Nagarjuna university, India Addis Ababa University, Ethiopia Adhiyamaan College of Engineering, India Advanced Technologies Application Center, Switzerland Afe Babalola University, Nigeria AFLAKMASR, Egypt Ain Shams University, Egypt Air University, Pakistan Ajayi Crowther University, Nigeria Ajou University, South Korea Akima, United States of America Akkineni Nageswara Rao College (ANR), India Al-Ahliyya Amman University, Jordan Al Al-Byte University, Jordan Al-Ameen Engineering College, India Al-Aqsa University, Palestine Al-Baha University, Saudi Arabia Al-Farabi Kazakh National University, Kazakhstan Al-Huson University College, Jordan Al-Maafer Community College, Saudi Arabia Al-Yamamah University, Saudi Arabia Al-Yarmouk, Jordan Alagappa Government Arts College, India Albaydha University, Yemen Albert School, France Alexandrian Technological Institute of Thessaloniki, Greece Al Hussein Technical University, Jordan Al Qalam University, Nigeria Algoma Univeristy, Canada Aliah University, India Aligarh Muslim University, India Alibaba, China Alliance University, India Alpha Al Fanar Group, Saudi Arabia Amazon, India American University of Sharjah, United Arab Emirates American University, Washington Amity University Rajasthan, India Amity University, India Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India Amrita Vishwa Vidyapeetham, India Amman Arab University, Jordan Amrita Vishwa Vidyapeetham University, India Amrita School of Engineering (Bengaluru), India Anand Engineering College, India Anant Netra, India Aneversete, India Anex Tour, Turkey Andhra University, India Ankara Yıldırım Beyazıt University, Turkey Annamacharya Institute of Technology and Science (AITS), India Anna University, India Antonine University (UA), Lebanon Apex consultancy, India APJ Abdul Kalam Technological University, India Arab Academy for Science, Technology and Maritime Transport (AASTMT), Egypt Arab Open University (AOU), Saudi Arabia Aristotle University of Thessaloniki, Greece Asia Pacific University of Technology & Innovation (APU), Malaysia Astana IT University, Kazakhstan Astro Pema AI, United States of America Association Of Independent Technological Universities (AITU), United States of America Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior (ABV-IIITM), India Auro, Dominican Republic Australian Department of Defence, Australia Azercell, AzerbaijanB
Bahauddin Zakariya University (BZU), Pakistan Baden-Wuerttemberg Cooperative State University (DHBW), Germny Bahrain Polytechnic, Bahrain Bahria University, Pakistan Banaras Hindu University, India Banco de Crédito del Perú, Peru Babylon University, Iraq Balochistan University of Information Technology and Engineering and Management Sciences (BUITEMS), Pakistan Bear_Hackers, China Bengkalis state polytechnic, Indonesia Bartin University, Turkey Basaheb Bhimrao Ambedkar University, India Başkent University, Turkey Beijing Information Science & Technology University, China Beijing Institute of Technology, China Beihang University, China Beijing University of Civil Engineering & Architecture (BUCEA), China Beijing University of Posts and Telecommunications, China Beijing University of Technology, China Beijing University, China Bennett University, India Betty Dubois, United States of America Bharatiya Engineering Science and Technology Innovation University, India Birla Institute of Technology, India Bishop's University, Canada Big Data and Intelligent Systems Laboratory, University of North Texas, United States of America Birla Institute of Technology And Science - Pilani (BITS), India Bingol University, Turkey Birmingham City University, United Kingdom Birkbeck University of London, United Kingdom Birla Institute of Technology and Science (Pilani), BlueLotus, Columbia BlueLotus, Turkey Bml Munjal University, India BMS Institute of Technology and Management, India Bocconi University, Italy Boğaziçi University, Turkey Boltech Consultancy, Kenya Bordeaux university, France Botho University, Botswana Bournemouth University, Rngland Bowen University, Nigeria Bowie State University, United States of America Bozorgmehr University, Iran BRAC University, Bangladesh Brandeis University, United States of America BRAINSTORM College, Ghana British College of Applied Studies, sri lanka British Columbia Institute of Technology (BCIT), Canada Brno University of Technology, Czechia Building Resources Across Communities (BRAC), Indonesia Burapha University, ThailandC
C. V. Raman Global University, India Ca' Foscari University of Venice, Italy Cadi Ayyad University of Marrakech, Morocco California State University (Fullerton), United States of America Canara Engineering College, India Cape Peninsula University of Technology, South Africa Capital University of Science and Technology, Pakistan Caplogy, France Carleton University, Canada Carnegie Mellon University (CMU), United States of America Catholic University of the Maule, Chile Catholic University of the Sacred Heart, Italy Centennial College, Canada Center for Cybersecurity, Bruno Kessler Foundation, Italy Central Connecticut State University, United States of America Central Institute of Technology Kokrajhar, India Central Queensland University, Australia Central South University, China C3iHub, India Central University of Finance and Economics, China Central University of Haryana, India Centre for Factories of the Future Ltd, United Kingdom Centre for Information and Communications Technology Research (CITIC), Spain Centro Universitário da FEI, Brazil Chandigarh University, India ChangChun University, China Chang'an University, China Charles University, Czechia Charles Sturt University, Australia Cheikh Anta Diop University of Dakar, Senegal Chiangmai University, Thailand Chicha Technology LLC, United States of America Chile university, Chile China Academy of Engineering Physics, China China West Normal University, China China Telecom, China China University of Petroleum(CUP), China Chinese Academy of Sciences, China Chittagong University of Engineering Technology (CUET), Bangladesh Chitkara University, India Chongoing University, China Chongqing University of Posts and Telecommunications, China Chonnam National University, South Korea Christ Academy Institute for Advanced Studies (CAIAS), India Cilacap State Polytechnic, Indonesia CINEC, Sri Lanka Cisco Systems Inc., India Cisco Systems Inc., United States of America City St George's, University of London, England Clark University, United States of America CMR Engineering College, India Cochin University of Science and Technology, India CodePath, United States of America COEP Technological University, India College of Charleston, United States of America College of Engineering Trivandrum, India Colorado Mesa University, United States of America Colorado State University, United States of America COMAC Shanghai Aircraft Manufacturing, China Comillah University, Bangladesh Community Safety Department (Prefectural Police Headquarters), United Kingdom Complex Systems Research Lab, Pakistan COMSATS Institute of IT, Pakistan COMSATS University Islamabad (CUI), Pakistan Conestoga College, Canada Connors State College, United States of America Covenant University, Canaanland Ota, Nigeria Council for Scientific and Industrial Research (CSIR), South Africa cPacket, United States of America CQVista Inc., South Korea Cranfield University, United Kingdom CUAB, Nigeria Czech technical university in Prague, Czech Cyber-citizen, United Kingdom Cyber Fix Ltd, Pakistan Cyber Fort, Nigeria CyberSec Data, South Africa Cyberspatial, United States of America Cyber Technology Institute De Montfort University, United Kingdom Cybersecurity Department, Federal University of Technology Akure (FUTA), Nigeria Cybersecurity Strategic Technology Centre, Singapore Cyber Silo Inc., United States of America Czech Technical University in Prague (CTU), Czech Cytech, PhilippinesD
Daejeon University, South Korea Daffodil International University (DIU), Bangladesh Dalhousie University, Canada Dalian Jiaotong University, China Dalian Maritime University, China Dalian University of Technology, China Dhanekula, India Dark, Pakistan Dayananda Sagar College of Engineering, India Dailylearning, Cameroon Defense Engineering College (DEC), Ethiopia Dele, Nigeria Datasafari Systems PTY LTD., Australia DAV University, India Deakin University, Australia Defence Academy of the United Kingdom, United Kingdom Defence Housing Authority (DHA) Suffa University, Pakistan Delft University of Technology (TU Delft), Netherlands Delhi Technological University, India Deloitte, Canada Deloitte, Japan Democritus University of Thrace, Greece Department of CSE IIT Madra, India Department of CSIS, BITS Pilani, India Department of multimedia technologies and telecommunications, Deputy Social Media Secretary, Pakistan Devis, United States of America Dhirubhai Ambani Institute of Information and Communication Technology (Daiict), India Drasil, Congo-Kinshasa Dr. Babasaheb Ambedekar Marathwada University, Iran Dr. Shariati Technical and Vocational College, Iran DS/S Computational Finance Inc., Canada Dublin city university, Ireland Dublin Business School (DBS), Ireland Dublin City University (DCU), Ireland Durban University of Technology, South Africa Durham College, Canada Duy Tan University, Vietnam DXC Technology, Italia D. Y. Patil International University, IndiaE
Early Code Training Institute, Nigeria East China University of Science, China East Delta University, Bangladesh Eastern Institute of Technology, New Zealand École Nationale d’Informatique et d’Analyse des Données (ENIAD), Morocco École Nationale d’Ingénieurs de Tunis (ENI)), Tunisia École Nationale des Sciences Appliquées de Tanger (ENSAT), Morocco École Nationale Supérieure d’Informatique (ESI), Algeria École Supérieure Privée d’Ingénierie et de Technologies (ESPRIT), Tunisia Edectus.com, Malaysia EFREI PARIS, France Egypt-Japan University of Science and Technology (E-JUST), Egypt Egyptian E-Learning University (EUI), Egypt Electronics and Telecommunications Research Institute (ETRI), Korea Embry-Riddle Aeronautical University, United States of America Emert, United Kingdom Emory University, United States of America Empresa Nacional de Aeroportos e Segurança Aérea, Republic of Cabo Verde Enliven Designers Pvt.Ltd, Sri Lanka Enugu State University of Science and Technology, Nigeria Epoka University, Albania EPI Digital School, Tunisia Equinix, United States of America Erlangen University, Germany Eskisehir Osmangazi University, Turkey ESTIN, AlgeriaF
Fachhochschule der Wirtschaft, Germany Faculdade de Ciências da Universidade do Porto, Portugal FAST National University of Computer and Emerging Science, Pakistan Federal University of Juiz de Fora (UFJF), Brazil Federal University of Pampa, Brazil Federal University of Pará (UFPA), Brazil Federal University of Technology Akure, Federal University of Viçosa (UFV), Brazil Federal University of Vicosa (UFV), Brazil Federation University Australia, Australia Feng Chia University, Taiwan FHDW Paderborn, Germany FireCompass, India FinTech, Tunisia Fitchburg State University, United States of America Flame University, India Florida Atlantic University, United States of America Fluminense Federal University, Brazil Foundation University School of Science and Technology (FUSST), Pakistan Fraunhofer Institute for Integrated Circuits IIS, Germany Free University Bozen-Bolzano, Italy French National Institute for Research in Digital Science and Technology, France Fudan University, China Funited Kingdomuoka Institute of Technology, Japan Fulda University of Applied Sciences, Germany Fundação Getulio Vargas (FGV), Brazil Fuzhou University, ChinaG
G-Info Technology Solutions Pvt. Ltd., India G H Patel College of Engineering & Technology, India Gachon University, Seongnam, Galgotias University, India Gandhi Institute of Technology and Management, India Gaston Berger University, Senegal Gazi University, Turkey General Assembly, Bahrain George Mason University, United States of America George Washington University, United States of America Georgia Tech University, United States of America German University In Cairo, Egypt Ghent University, IDLab-Imec, Belgium Gitam University, India Gokaraju Rangaraju Institute of Engineering and Technology (GRIET), India Google, United States of America Google, Pakistan Government Engineering College, Kozhikode, India Government College University, Pakistan Govind Ballabh Pant Institute of Engineering and Technology (GBPID), India Graphic Deemed University, India Graphic Era University, India Grand Canyon University, United States of America Green University of Bangladesh, Bangladesh Griffith University, Australia GTB Technologies, Inc., United States of America Gunadarma University, Indonesia Guangdong Pharmaceutical University, China Guangdong University of Technology, China Guangxi Key Laboratory of Cryptography and Information Security, China Guangzhou University, China Guelph University, Canada Guilin University of Electronic Technology, Iran Gujarat Technological University, India Guru Govind Singh University, IndiaH
Hacettepe University, Turkey Hadhramout University, Yemen Hainan Normal University, China Halmstad University, Sweden Hangzhou Dianzi University, China Hanoi University of Science and Technology (HUST), Vietnam Haramaya University, Ethiopia Harbin Institute of Technology, China Harward university, United Stated of America Headligt Solutions, Romania Heilongjiang University, China Helwan University, Egypt Henan Key Laboratory of Network Cryptography Technology, China Henan Police College, China Hewlett-Packard, United States of America Higher Education Commission (HEC), Pakistan Higher Technology Institute, Egypt Hillstonenet Co., China Hindushthan College of Engineering and Technology, India Hitech Solutions (Pvt) Ltd., India HIRU TV, Sri Lanka Ho Chi Minh City University of Technology (HCMUT), Vietnam Hochschule Fulda University of Applied Sciences, Germany Hochschule für Telekommunikation Leipzig (HfTL), Germany Hogeschool van Amsterdam, Netherlands Hohai University, China Hokkaido University, Japan Holy Angel University, Philippines Horizon17 Technology and Sustainability Pvt Ltd., India HPE Networking, United States of America Huazhong University of Science and Technology, China Hulian, China Higher Technological Institute (HTI), Egypt Humber polytechnic, Canada Humboldt University of Berlin, Germany Hunan University of Humanities, China HVA International, NetherlandsI
i-eng, Cameroun i4 Ops, United States of America IIIT Surat, India IBM Research, United States of America Ibn Tofail University, Morocco ICAR-CNR (ITALY), Italy ICT Convergence Research Center, China IMT School for Advanced Studies Lucca, Italy Instituto Nacional de AstrofĂsica, Ă“ptica y ElectrĂłnica (INAOE), Mexico Institute of Integrated Learning in Management (IILM) University, India Institute of Information Engineering (IIE), China Insane Cyber, United States of America Institute Polythecnic National (IPN), Mexico IIT BHU, India Illinois Institute of Technology, United States of America Imam Abdulrahman Bin Faisal University, Saudi Arabia Imperial College London, United Kingdom IMP Group International, Canada Independent university of Banja LUnited Kingdoma, Bosnia and Herzegovina Indian Institute of Information Technology, India Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, India Indian institute of Information Technology Pune India, India Indian Institute of Science, India Indian Institute of Technology (IIT) Dharwad, India Indian Institute of Technology Guwahati, India Indian Institute of Information Technology (IIIT) Kottayam, India Indian Institute of Technology (BHU) Varanasi, India Industrial Technology Research Institute, Hsinchu, Taiwan Industrial University of Ho Chi Minh city, Vietnam Informatics Institute of Technology, Sri Lanka Innopolis University, Russia Insight Cyber Inc., United States of America International University of Business Agriculture and Technology (IUBAT), Dhaka, Bangladesh Institut Superieur d'Informatique de Mahdia, Tunisia Institut supĂ©rieur de l'Ă©lectronique et du numĂ©rique, France Institute for Advanced Study (IAS), United States of America Institute for Development and Research in Banking Technology (IDRBT), India Institute for Infocomm Research (I2R), Singapore Institute for Information and Communication Technologies, Canada Institute of Information Technology of ANAS, Azerbaijan Institute of Information Engineering Chinese Academy of Sciences Beijing, China Institute of Technology of Cambodia, Cambodia Institut Teknologi Bandung, Indonesia Instituto Militar de Engenharia, Brazil Instituto Superior de CiĂŞncias de Educação da HuĂla (ISCED-HuĂla), Angola Instituto Federal de Educação, CiĂŞncia e Tecnologia do Ceará (IFCE), Brazil Instituto Federal de Educação, CiĂŞncia e Tecnologia do Rio Grande do Sul (IFRS), Brazil Integer Ltd, india InTec, France Intelligent Policing Key Laboratory of Sichuan Province, Sichuan Intelliswarm, Switzerland International Burch University (IBU), Bosnia and Herzegovina International Hellenic University, Greece IPN, Mexico IQRA University - Islamabad Campus (IUIC), Pakistan islab, Republic of Korea IRISA - Institut de Recherche en Informatique et Systèmes AlĂ©atoires, France Ironstone Systems LLC, United States of America Islamic Azad University, Iran Islamic Azad University Najafabad Branch, Iran Islamia College University Peshawar, Pakistan Istanbul Technical University, Turkey IT-Convergence Engineering, South Korea Ivan Franko National University of Lviv, United Kingdomraine Izmir University of Economics, TurkeyJ
J.C. Bose University of Science and Technology, YMCA (Formerly YMCA UST), India j3scandjove, Nigeria Jagannath University, Bangladesh Jadavpur University, India Jahangirnagar University, Bangladesh Jain University, India Jawaharlal Nehru Technological University Hyderabad, India Jenderal Soedirman University, Indonesia Jinan University (JNU), ChinaJiangsu Province Engineering Research Center of Security for Ubiquitous Network, China Jiangsu University, China Jinan University, China Johannes Kepler University Linz, Austria John Jay College of Criminal Justice, United States of America Johns Hopkins University, United States of America Jomo Kenyatta University Of Agriculture And Technology, Kenya Jordan University of Science & Technology (JUST), Jordan JoĹľef Stefan Institute, Slovenia Jubail Industrial College, Saudi Arabia
K
K J Somaiya Institute of Technology, India Kapadokya University, Türkiye Kadir Has University, Turkey Kansas State University (KSU), United Stated of America Kapadokya Universty, Turkey Kaplan Business School, Australia Karabük University, Turkey Karnatak Lingayat Education Technological University, India Karunya University, india Keele University, United Kingdom Keldysh Institute of Applied Mathematics (KIAM), Russia Kennesaw State University, United States of America Kent State University, United States of America Khalifa University, United Arabic Emirates Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Pakistan King AbdulAziz University, Saudi Arabia King Fahad University of Petroleum and Minerals, Saudi Arabia King Fahad University, Saudi Arabia King Fahd University of Petroleum & Minerals, Saudi Arabia King Faisal University, Saudi Arabia King Khalid University, Saudi Arabia King Mongkut Institute of Technology Ladkrabang, Thailand King Saud University, Saudi Arabia KL University, India KLE Technological UniversityKMUTNB, Thailand Kokurakita Police Station, FUnited Kingdomuoka Prefectural Police, Iapan Koneru Lakshmaiah University (KL University), India Kongu Engineering College, India Kookmin University, South Korea Korea Institute of Energy Technology (KENTECH), South Korea Korea Internet & Security Agency (KISA), South Korea Korea National Defense University, South Korea Kriya, India KPR Institute of Engineering and Technology, India KTH Royal Institute of Technology, Sweden KU Leuven, Belgium Kumoh National Institute of Technology, South Korea Kütahya Dumlupınar üniversitesi, Turkey Kuwait University, Kuwait Kwame Nkrumah University of Science and Technology (KNUST), Ghana Kyungwoon University, South Korea Kyushu University, JapanL
La Salle University – Ramon Llull, Spain Laboratoire Informatique, Image et Interaction (L3i), France Laboratory of Numerical Analysis and Computer Science, Italy Lahore Leads University (LLU), Pakistan Lancaster University, United Kingdom Lanzhou University, China Lawrence Livermore National Laboratory, United States of America Lawrence Technological University (LTU), United States of America Le Quy Don Technical University, Vietnam Lebanese American University (LAU), Lebanon Lebanese University, Lebanon Lemon IT, Indonesia Letshego Holdings, Namibia Libtan Acadmy, Libya Load Go Transport Inc., Canada Los Coco, Dominican Republic Loughborough University London, Lovely Professional University, India Lush Horizon, United States of America Lummetry AI, Finland Ludwig-Maximilians-University, Germany Lxav, Cameroon Lynntech Inc., United States of AmericaM
M.Kumarasamy College of Engineering (MKCE), India Madurai Kamaraj University, India Madhav Institute of Technology and Science (MITS), India Maharaja Sriram Chandra Bhanja Deo University, India Maulana Abul Kalam Azad University of Technology (MAKAUT), India Mahidol University, Thailand Majmaah University, Saudi Arabia Makerere University, Uganda Malaya University, malaysia Malaviya National Institute of Technology Jaipur (MNIT), India Malta College of Arts, Malta Malmö University, Sweden MalScanXSentinel, Pakistan Manchester Metropolitan University, United Kingdom Manipal Institute of Technology Bengaluru, India Manipal University Jaipur, India Mansoura University, Egypt Mapúa Malayan Colleges Mindanao, Philippines Marathwada University, India Marmara university, Turkey Marwadi University, India MATS University, United States of America Materials and Chemistry Convergence Technology, South Korea Maven Technical, Nigeria Medcare MSO, Pakistan Medizinische Hochschule Brandenburg (MHB) University, Germany Menoufia University, Egypt Mersin University, Turkey Metropolitan University of Technology,chile McGill University, Canada Michigan Technological University, United States of America Microsoft India Development Center, India Middle East Technical University in Ankara, Middle Tennessee State University, United States of America Misr International University, EgyptMississippi State University, United States of America MIT Arts, Commerce & Science College, India MIT College of Engineering, India MIT Lincoln Laboratory, United States of America MITRE.org, United States of America Mizoram University, India Mojass Sdn. Bhd., Malaysia Monash University, Indonesia Montclair State University, United States of America Morgan State University, United States of America Moscow Institute of Physics and Technology, Russia Moscow State University of Technology (Stankin), Russia Mother Teresa Women’s University, India Motilal Nehru National Institute of Technology, India MSIS Department of Rutgers University, United States of America Multimedia University, Malaysia Mumbai University, India
Murdoch University, Australia MyCoy LLC, Malaysia
N
Najaf Abad University, Iran Najran University, Saudi Arabia Namal University Mianwali, PakistanNanjing Tech University, China
Nanjing University of Science and Technology (NJUST), China Nara Institute of Science and Technology (NARA), Japan National Centre For Cyber Security – UET Peshawar (NCCS-UETP), Pakistan
National Chiao Tung University, Taiwan National Chung Cheng University, Taiwan National College Of Business Administration And Economics, Pakistan National College of Ireland, Ireland National Dong Hwa University, Taiwan National Forensic Sciences University (NFSU), India
National Higher Polytechnic Institute (NAHPI), Cameron National Institute of Technology (NIT), India National Institute of Technology Karnataka (NITK), India National Institute of Technology Sikkim, India National Institute of Technology Tiruchirappalli (NITT), India National Kaohsiung University of Science and Technology, Taiwan National School of Artificial Intelligence, Algeria National Skills University Islamabad, Pakistan National Sun Yat-sen University, Taiwan
National Taipei University of Science and Technology, Taiwan National Taiwan University, Taiwan
National Taiwan University of Science and Technology, Taiwan
National Technological University of South Lima, Peru
National Telecom Regulatory Authority (NTRA), Egypt National University of Computer and Emerging Sciences, Pakistan National University of Defense Technology (NUDT), China National University of Defense Technology Changsha, China National University of Defense Technology, China National University of Malaysia (UKM), Malaysia National University of Sciences and Technology (NUST), Pakistan National University of Technology (NUTECH), Pakistan
National University Singapore (NUS), Singapore National University of Sciences & Technology (NUST), Pakistan National University of Sciences & Technology, School of Electrical Engineering and Computer Science (NUST-SEECS), Pakistan
National Skills University Islamabad, Pakistan National Chengchi University (NCCU), Taiwan NDesign, Canada National Taipei University, Taiwan National Taipei University of Technology (NTUT), Taiwan National Yunlin University of Science and Technology, Taiwan Necmettin Erbakan University, Turkey Nehemiah Security Co., United States of America New Mansoura University, Egypt
New Mexico State University, United States of America New York University (NYU), United States of America New York University Abu Dhabi (NYU), United Arabic Emirates New York University Tandon, United States of America NIELIT Calicut, India
Nha Trang University, Vietnam Nigeria Federal University of Technology, Nigeria NiKaDa Technologies Pvt. Ltd., India NIIT University, India Nile University, Egypt NIMS University, India Ninevah University, Iraq Nirma University, India NIT Raipur, India Nitte Meenakshi Institute of Technology, India Nirma University, India
Nitte Meenakshi Institute of Technology (NMIT), India NMAM Institute of Technology, India NMIMS (Narsee Monjee Institute of Management Studies), India Netscout Systems, India North Carolina State University, United States of America
North Carolina Agricultural and Technical State University, United States of America North China Electric Power University, China North Dakota State University (NDSU), United States of America Northeastern University (University in Shenyang), China Northeastern University, United States of America
North South University, Bangladesh Northumbria University, United Kingdom Northwestern Polytechnical University, China Northwestern University, United States of America Norwegian University of Science and Technology, Norway Notre Dame University, Bangladesh NOVA IMS, Portugal Noavaila, United Kingdom Natco, Yemen Novasibirsk State University, Russia
O
Oak Ridge National Laboratory, United states of America OAKLAND Univeristy, Newzealand Old Dominion University, United States of America Open Code Evolution, United States of America Open University of Netherlands, Netherlands Opcia, South Korea Osaka University, Japan Oracle Lab, Canada Oriental University, India Oslo Metropolitan University, NorwayP
Pabna University of Science and Technology, BangladeshPacific Northwest National Laboratory, United States of America Packetwerk, Germany Pakistan Institute of Engineering and Applied Sciences, Pakistan Panimalar, India Panjab University, Chandigarh, India Papua New Guinea University of Technology - University in Kainantu, Papua New Guinea Parahyangan catholic university, Indonesia
Parmy technologies pvt ltd, India Pathumthani Institute of Technology (PIT), Thailand Patuakhali Science and Technology University, Bangladesh Peking University, China Penn State University, USA Pennsylvania State University, USA Pentangularit, Bangladesh People's Public Security University of China (PPSUC), china PeopleTec Inc., United States of America PES Institute of Technology and Management, India PES College of Engineering, India PES University, India Politechnika Krakowska, Poland Politecnico di Bari, Italy Politecnico di Torino (POLITO), Italy Petroleum-Gas University of Ploiesti, Romania PLA Information Engnieering University,Zhengzhou,china PLUS SECURE DE MEXICO, Mexico Polish-Japanese Academy of Information Technology, Poland Politecnico di Milano, Italy Politeknik Negeri Cilacap, Indonesia Politeknik Statistika STIS, Indonesia Political Elektronika Negeri Surabaya (PENS), Indonesia Pölten University of Applied Sciences, Austria Polytechnic Institute of Leiria (IPLeiria), Portugal Polytechnique MontrĂ©al, Canada PolyU, Poland PontifĂcia Universidade CatĂłlica de Minas Gerais - Coração EucarĂstico, Brazil Posts and Telecommunications Institute of Technology (PTIT), Vietnam prakash industries, India Premier University, Bangladesh Presidency University, India Prince Mohammad Bin Fahd University, Saudi Arabia Prince of Songkla University, Thailand Prince Sultan University, Saudi Arabia Prince Sattam Bin Abdulaziz University, Saudi Arabia
Princess Nourah Bint Abdul Rahman University, Saudi Arabia
Princess Sarvath Community College, Jordan Princess Sumaya University for Technology (PSUT), Jordan Princeton University, USA PSG College of Technology, India
PSL Research University, France
PUC Minas, Brazil Punjab University, Pakistan Purdue University, India Purdue University, United States of America Purple Mountain Laboratories, China PUnited States of American National University (PNU), South Korea
Q
Qassim University, Saudi Arabia Qatar university, Qatar Qingdao University, China Quantea.com, United States of America Queen’s University Belfast, United Kingdom Queen mary University of London, United Kingdom Queens College, United States of America Queensland University of Technology (QUT), AustraliaR
R.V. College of Engineering Bengaluru, India Radboud University, Netherlands rAIxon, United Kingdom Rajarambapu Institute of Technology, India Rajshahi University of Engineering & Technology, Bangladesh RIT (Rajiv Gandhi Institute of Technology), India Recep Tayyip ErdoÄźan University, Turkey Rentile, Czech Rensselaer Polytechnic Institute (RPI) - School of Science, United States of America Reva University, IndiaRey Juan Carlos University, Spain Rice University, United States of America
RISE, Sweden Royal Melbourne Institute of Technology (RMIT), Australia Rochester Institute of Technology, United States of America Royal Military College of Canada, Canada Royal University of Phnom Penh, Cambodia RV Institute of Technology and Management (RVITM), India RUDRA Cybersecurity, India
S
S.E.A Protection Services, CanadaSĂŁo Paulo State University, Brazil
Sabanci University, Turkey Sacred Heart University (SHU), United States of America
Sadhu Vaswani Institute of Management Studies (SVIMS), India SAL Institute of Technology and Engineering Research, India Sakarya University of Applied Sciences, Turkey Sam Houston State University, United States of America Sama Partners Business Solutions, Tunisia Samsun Ăśniversitesi, Turkey San Jose State University, United States of America SĂŁo Paulo State University, Brazil Sardar Vallabhbhai Patel Institute of Technology (SVIT), India Saraswati College of Engineering, Navi Mumbai, India Sardar Patel Institute of Technology, India
Sathyabama Institute of Science and Technology, India Saveetha Engineering College, India School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China School of InfoComm Technology, Singapore Science and Technology (MCAST), Malta secunet Security Networks AG, Germany Securite Inc., United States of America SecureITlab, Bahrain
Sejong University, South Korea SelçUnited Kingdom Üniversitesi, Turkey
Seneca College of Applied Arts and Technology, Canada Seokyeong University, South Korea Seoul Women’s University, South Korea Shaanxi Normal University, China Shahid Bahonar University of Kerman, Iran Shahid Beheshti University, Iran
Shahid Rajaee University, Iran Shahid Sattari Aviation University, Iran Shandong University, China
Shandong Normal University, China Shanghai Jiao Tong University, China Shanxi University of Finance and Economics, China Sharda University, India Sharif University of Technology, Iran Shaqra University, Saudi Arabia Scheduled Caste Welfare (SSWH), India Sheffield Hallam University, United Kingdom Shenyang Ligong University, China Shenzhen university, Yaman Shiraz University, Iran Shivaji University, India Siberian Federal University, Russia
Sichuan Normal University, China Sichuan University, China Sictel, Mexico Sikkim Manipal Institute of Technology, India Siliguri Institute of Technology, India Simon Fraser University, Canada Singapore Management University (SMU), Singapore
Sir Syed CASE Institute of Technology, Pakistan Sistan and Baluchestan Univerisity, Iran SJB Institute of Technology (SJBIT), India Skyline University College, United Arab Emirates Slovak University of Technology, Slovakia SMU Dedman School of Law, United States of America SoftCrafted.com, USA Sol Plaatje University, South Africa Sole, Hungary Soongsil University, South Korea South China University of Technology, China South East Technological University, Ireland South Ural State University, Russia Southeast University (SEU), China South China University of Technology, China
Southeast University, Bangladesh Southeast University (SEU), China
Southwest Petroleum University, China Sri Krishna Arts and Science College, India
Sir Padampat Singhania University (SPSU), India Sri Ramachandra Institute of Higher Education and Research, India Sri Ramaswamy Memorial Institute of Science and Technology (SRM), India
Sri Sivasubramaniya Nadar College of Engineering, India
Sri Institute of Science and Technology (SIST), India Sri Lanka Institute of Information Technology (SLIIT), Sri Lanka Srinivas University, India
SRK institute of technology, India SRM Institute of Science and Technology, India Ss. Cyril and Methodius University in Skopje (UKIM), North Macedonia ST Engineering, Singapore St. Joseph's College of Engineering, Chennai, India St. Petersburg State University, Russia St. Polten University of Applied Sciences, Austria Staffordshire University, United Kingdom Stanford University, United States of America
State University of Londrina, Brazil State Polytechnic of Ujung Pandang, Indonesia
STMIK AMIK Riau, Indonesia STMIK Widya Utama, Indonesia STU University, Iraq Strategic Support Force Information Engineering University, China Sulaimani Polytechnic University, Iraq Sun Yat-sen University, China Sungkyunkwan University, South Korea Supercomputer Education and Research Center Supercomputer Education and Research Center, India Surabaya State Polytechnics, Indonesia Symbiosis Centre for Information Technology, India Swe, United Kingdom
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie, Poland
T
Tai Solarin University of Education, Nigeria Taibah University, Saudi ArabiaTaif University, Saudi Arabia Taiz University, Yemen Tallinn University of Technology, Estonia Tarbiat Modares University, Iran Tata Consultancy Services (TCS), India Technical and Vocational University, Shariaty Technical College, Iran
Technical University of Cluj-Napoca, Romania Technical University of Dresden, Germany Technical University of Madrid, Spain Technical University of Munich, Germany Technological University of Panama, Panama
Technological University of the Shannon, Ireland techtones, Nigeria Telecom Bretagne, France Telkom University, Indonesia
Telecommunications and Applications Cheikh Anta Diop University, Senegal Telkon University, Indonesia Tennessee State University, United States of America Texas A&M University Kingsville, United States of America Teyliom Group, Senegal Thamar University, Yemen Themis AI Inc., United States of America The Alan Turing Institute, United Kingdom The Maharaja Sayajirao University of Baroda, India The Open University, United Kingdom The Sight Technology Hub, Nigeria The University of Adelaide, Australia The University of British Columbia (UBC), Canada The University of Hong Kong, Hong Kong The University of Jordan, Jordan The University of Manitoba, Canada The University of Sydney, Australia The University of Tokyo, Japan The World Islamic Sciences and Education University, Jordan Thiagarajar College, India Third Sarl., Cameroon
Tianjin University, China Tishreen University, Syria
TNT Prowash and Preservation, Scotland Todyl Inc, United States of America
Tokyo Institute of Technology, Japan Tokyo Metropolitan University, Japan Tomsk Polytechnic University, Russia Toronto Metropolitan University (TMU), Canada Tongji University, China
Trakya University, Turkey Trideum Corporation, United States of America Tshimologong Precinct - Technology Park, South Africa
Tsinghua University, China
Tunku Abdul Rahman University of Management and Technology, Malaysia Turkish - German University, Turkey TU Wien, Austria
Tyonek Services, United States of America
U
UAF sub campus burewala, Pakistan UCAS, BangladeshUCL, United States of America
UCSD, US Ulster University, United Kingdom
Ultra-Intelligent Computing/Communications Lab, Chung-Ang University, South Korea UiTM, Malaysia
Umm Al-Qura University, Saudi Arabia UNED, Spain
Unesp – São Paulo State University, Brazil
UNITAR International University (UNITAR), Malaysia United Arab Emirates University, UAE United Commercial Bank PLC, Bangladesh
United States Military Academy, United States of America United Technologies Research Center (UTRC), United States of America Univerisyt of Twente, Netherlands Universidad Carlos III de Madrid, Spain, Universidad de Alcala (UAH), Spain Universidad de Cádiz, Spain Universidad de la Laguna, Spain Universidad del Cauca, Spain Universidad Diego Portales, Spain Universidad Nacional de Asunción, Spain Universidad Nacional de Educación a Distancia (UNED), Spain Universidad Nacional de Mar del Plata, Argentina Universidad Nacional de San Martin, Argentina Universidad Politécnica Salesiana (UPS), Ecuador Universidad Pontificia Bolivariana (UPB), Colombia Universidad Popular Autónoma del Estado de Puebla (UPAEP), Mexico
Universidad San Francisco de Quito, Ecuador Universidad TĂ©cnica Federico Santa MarĂa (USM), Chile Universidade da Coruna, Spain Universidade de SĂŁo Paulo, Brazil Universidade Estadual de Maringá, Brazil Universidade federal De lavras, Brazil Universidade Federal de Santa Catarina (UFSC), Brazil Universidade Federal do Ceará (UFC), Brazil Universidade Federal do MaranhĂŁo (UFMA), Brazil Universidade Federal Fluminense (UFF), Brazil Universidade Regional de Blumenau, Brazil Universidade de SĂŁo Paulo, Brazil Universita degli Studi di Messina, Italy UniversitĂ degli Studi di Napoli Federico II, Italy UniversitĂ degli Studi Roma Tre, Italy UniversitĂ di Palermo, Italy UniversitĂ degli studi di Palermo, Italy Universita Della Calabria, Italy Universitaet Klagenfurt, Austria Universitas Bina Sarvana Informatika, Indonecia Universitas Ciputra Surabaya, Indonesia Universitas Royal, Indonesia Universitas Pelita Harapan, Indonesia Universitas Pamulang, Indonesia Universitas Siliwangi, Indonecia
Universitas Trunojoyo Madura, Indonesia Universitat Politècnica de València (UPV), Spain Université Bretagne Loire, France Université de Moncton, Canada Université de Sherbrooke, Canada
Université du Quebec en Outaouais (UQO), Canada Université Internationale des Technologies de l'Information, Kazakhstan Université Paris-Saclay, France Université Saint-Joseph de Beyrouth (USJ), lebanon Universiti Kuala Lumpur, Malaysia Universiti Putra Malaysia, Malaysia Universiti Sains Malaysia (USM), Malaysia Universiti Technology Malaysia, Malaysia Universiti Teknologi PETRONAS, Malaysia Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia
University at Buffalo, United States of America University Carlos, Spain University Catholic of Maule, Chile University Cobur of Germany, Germany University College Dublin, Ireland University College London, United Kingdom University Kasdi Merban Ouargla, Algeria University Malaysia Pahang, Malaysia University Mohammed 5 Rabat, Morocco
University of Abuja, Nigeria University of Auckland, New Zealand University of Aegean, Greece University of Aizu, Japan University of Alcalá de Henares, Spain University of Amsterdam, Netherlands University of Applied Sciences Munich (UDBW), Germany University of Applied Sciences Wedel, Germany University of Babylon, Iraq University of Bahrain, Bahrain University of Bari “Aldo Moro”, Italy University of Birmingham Dubai, United Arab Emirates University of BrasĂlia, Brazil University of Bridgeport, United States of America University of Bristol, United Kingdom University of Cádiz, Spain
University of Calabria, Italy
University of California San Diego, United States of America
University of Calgary, Canada University of Campania, Italy
University of Caxias do Sul, Brazil
University Of Central Punjab, Pakistan University of Chinese Academy of Sciences (UCAS), China University of Cincinnati, United States of America University of Colombo, Colombia
University of Colorado, United States of America University of Cyprus, Cyprus University of Dayton, United States of America University of Delhi, India
University of Dodoma, Tanzania
University of Doha for Science an Technology, Qatar University of East London, United Kingdom
University of Economics and Innovation in Lublin, Poland University of Edinburgh, United Kingdom University of Electronic Science and Technology of China, China University of Engineering & Applied Sciences (Swat), Pakistan
University of Engineering and Technology (UET), Taxila, Pakistan
University of Essex, United Kingdom University of Exeter, United Kingdom University of Ferrara, Italy University of Fort Hare, South Africa University of FuZhou, China University of Glasgow, United Kingdom University of Guelph, Canada
University of Granada, Spain University of Greenwich, United Kingdom University of Haripur, Pakistan University of Health Sciences,Istanbul, Turkiye University of Hertfordshire, United Kingdom University of Hohenheim, Germany University of Houston, United States of America University of Houston, United States of America
University of Hyderabad, India University of Hyogo, Japan University of Indonesia, Indonesia University of Information Technology, Ho Chi Minh city, Vietnam University of Isfahan, Iran University of Jaén, Spain
University of Johannesburg, South Africa University of Kashmir, Pakistan
University of Kelaniya, Sri Lanka
University of Kerala, India University of Kufa, Iraq
University Of Lahore, Pakistan University of Lay Adventists of Kigali, Rwanda University in Leuven, Belgium University of Liberal Arts Bangladesh (ULAB), Bangladesh University of Liechtenstein University of Limpopo, South Africa University of Ljubljana, Slovenia University of London, United Kingdom University of Luxembourg, Luxembourg University of Magallanes, Chile University of Malta, Malta
University of Maryland, United States of America University of Maryland, United States of America University of Melbourne, Australia University of Mines and Technology, Ghana University of Mostar, Bosnia and Herzegovina University of Muhammadiyah Malang, Indonesia University of New Haven, United States of America University of New Mexico (UNM), Mexico
University of New South Wales, Australia University of Niagara Falls, Canada University of North Carolina at Charlotte, United States of America University of Ontario, Canada University of Oregon, United States of America University of Oslo, Norway University of Ottawa, Canada University of Oviedo, Spain University of Oxford, United Kingdom University of Padua, Italy University of Pennsylvania, United States of America University of Pretoria, South Africa University of Quebec, Canada University of Rwanda, Rwanda University of Reading, United Kingdom
University of Riau, Indonesia
University of Salamanca, Spain University of Salford, United Kingdom University of Sannio, Italy University of Santa Cruz Do Sul, Brazil University of SĂŁo Paulo, Brazil
University of Science and Technology Chittagong, Bangladesh
University of Science and Technology Beijing, China University Of Science And Technology Houari Boumediene, Algeria University of Science and Technology of China, China University of Sfax, Tunisia University of South Carolina, United States of America University of Southern Queensland, Australia University of Sri Jayewardenepura, Sri Lanka University of Staffordshire, United Kingdom University of St. Andrews, United Kingdom University of Stavanger, Norway University of Stirling, United Kingdom University of Strathclyde Glasgow, United Kingdom University of Surabaya University of Surrey, United Kingdom University of Swabi, Pakistan University of Tabriz, Iran University of Technology - Mauritius, South Africa University of Technology of Baghdad, Iraq University of Tehran, Iran University of Texas at Dallas (UTDallas), United States of America University of Texas at San Antonio, United States of America University of the Chinese Academy of Sciences, China University of Toronto, Canada
University of Trento, Italy University of Twente, Netherlands University of Vale do Rio dos Sinos Campus SĂŁo Leopoldo (Unisions), Brazil University of Virginia, United States of America University of Waikato, New Zealand
University of Warwick, United Kingdom University of Washington, United States of America University Of Waterloo, Canada University of West Florida, United States of America University of West London - Ras Al Khaimah Campus, United Arab Emirates University of Western Ontario, Canada University of Windsor, Canada University of Wisconsin–Eau Claire, United States of America University of Wisconsin-Parkside, United States of America University of Wisconsin-Madison, United States of America University of Wurzburg, Germany University of Zürich, Switzerland
Universitat Politecnica de València, Spain University Putra Malaysia, Malaysia University Saience Malaysia (USM), Malaysia
University Sultan Moulay Slimane, Morocco University Utara Malaysia, Malaysia UNOB, Slovakia
UNOC, Czech Republic
UNSW Sydney, Australia
UPAEP, Mexico UPC BarcelonaTech Vilanova, Spain Uwieazy Inc., Indonesia
V
Velagapudi Ramakrishna Siddhartha Engineering College, India Vellore Institute of Technology (VIT), India Veritas University Abuja, Nigeria Viavi Solutions, United States of America Vibgyor, India Victoria University of Wellington, New Zealand Vietnam National University, Vietnam Vietnam National University - University of Engineering and Technology (VNU-UET), Vietnam Vietnamese-German University, Vietnam Vishwakarma University, India VIT-AP, India VRSEC, India Virtual Barter and Trade, CanadaW
Walailak University, Thailand Warsaw University of Technology, Poland Waseda University, Japan Wayne State University, United States of AmericaWEP-TECH, Cameroon Westpac, Australia
Western University, Canada Western Kentucky University, United States of America Western Washington University (WWU), United States of America WHU, Germany Widyatama University, Indonesia Wolkite University, Ethiopia Women's Christian College, India Wonkwang University (WKU), South Korea Wroclaw University of Science and Technology, Poland
Wright State University, United States of America Wuhan university, China
Wuhan Textile University, China Wuhan University, China
X
Xi’an University of Technology, ChinaXi'an Jiaotong University, China Xidian University, China
Y
YANDEX Co., Russia YarmoUnited Kingdom University (School of Information Technology and Computer Science), Jordan YAYAFOOD, Nigeria YemenSoft, Yemen, Armenia Yerevan State University Yeshiva University, United States Yeditepe University, Turkey Yildiz Technical University, Turkey Yonsei University, South Korea Yuan Ze University, TaiwanZ
Zaalis, France Zagazig University, Egypt Zarqa University, Jordan Zealand - Sjællands Erhvervsakademi, Denmark Zetech University, Kenya Zhengzhou University (ZJU), China Zhejiang Gongshang University, China ZIGHRA Co., Canada
Researchers named among top researchers for Canada 150
The cybersecurity Research and Academic Leadership award, Canada 2019
The cybersecurity academic award, Canada 2017