Arash Habibi Lashkari
Associate Professor

Designed by Farnaz Towhidi

CYBERSECURITY DATASETS

Block Chain, Decentralized Finance (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 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.

IoT Intrusion Detection and Malware Analysis 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.

Non-IoT Intrusion Detection and Malware Analysis 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 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.

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 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.

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 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 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 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.

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.

Malicious DNS and DoH 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.

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). 


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.

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.

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.

Encrypted Traffic and Dark Net Datasets:

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.

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.


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.


Smart Phones Security Datasets:

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.

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.

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 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.


Malicious URL Datasets:

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.


Malicious PDF Datasets:

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.

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.

Software Security 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.

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.

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A

A&M College, United States of America
Aalborg University, Denmark
 Aalim muhammed salegh college of engineering, India
Aalto university, Finland
Abdullah Gul University (AGU), Turkey
Abudhabi University, United Arab Emirates
Academia de Studii Economice din Bucuresti (ASE), Romania
Acharya Nagarjuna university, India
Addis Ababa University, Ethiopia
Adhiyamaan College of Engineering, India
Advanced Technologies Application Center, Switzerland
Ain Shams University, Egypt
Air University, Pakistan
Al-Ameen Engineering College, India
Al-Aqsa University, Palestine
Al-Baha University, Saudi Arabia
Al-Huson University College, Jordan
Al-Maafer Community College, Saudi Arabia
Alagappa Government Arts College, India
Albaydha University, Yemen
Alexandrian Technological Institute of Thessaloniki, Greece
Al Hussein Technical University, Jordan
Aliah University, India
Aligarh Muslim University, India
Algoma Univeristy, Canada
Amazon, India
American University of Sharjah, United Arab Emirates American University, Washington
Amity University Rajasthan, India
Amity University, India
Amman Arab University, Jordan
Amrita Vishwa Vidyapeetham University, India
Amrita School of Engineering (Bengaluru), India
Anand Engineering College, India
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
Aristotle University of Thessaloniki, Greece
Asia Pacific University of Technology & Innovation (APU), Malaysia
Australian Department of Defence, Australia
Azercell, Azerbaijan

B

Baden-Wuerttemberg Cooperative State University (DHBW), Germny
Bahria University, Pakistan
Banaras Hindu University, India
Basaheb Bhimrao Ambedkar University, India
Beijing Information Science & Technology University, China
Beijing Institute of Technology, China
Beihang University, 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
Big Data and Intelligent Systems Laboratory, University of North Texas, United States of America
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
Bocconi University, Italy
Boğaziçi University, Turkey
Boltech Consultancy, Kenya
Bordeaux university, France
Bournemouth University, Rngland
Bowie State University, United States of America
Bozorgmehr University, Iran
Brandeis University, United States of America
British Columbia Institute of Technology (BCIT), Canada
Brno University of Technology, Czechia
Burapha University, Thailand

C

Ca' Foscari University of Venice, Italy
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
Carnegie Mellon University, 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
Central University of Finance and Economics, China
Central University of Haryana, India
Centre for Information and Communications Technology Research (CITIC), Spain
Centro Universitário da FEI, Brazil
Chandigarh University, India
ChangChun University, China
Charles University, Czechia
Charles Sturt University, Australia
Cheikh Anta Diop University of Dakar, Senegal
Chile university, Chile
China Academy of Engineering Physics, China
China West Normal University, China
China Telecom, China
Chinese Academy of Sciences, China
Chitkara University, India
Chongoing University, China
Chongqing University of Posts and Telecommunications, China
Christ Academy Institute for Advanced Studies (CAIAS), India
CINEC, Sri Lanka
Cisco Systems Inc, United States of America
Cochin University of Science and Technology, India
CodePath, United States of America
College of Charleston, United States of America
College of Engineering Trivandrum, India
Colorado Mesa University, United States of America
COMAC Shanghai Aircraft Manufacturing, China
Comillah University, Bangladesh
Community Safety Department, FUnited Kingdomuoka Prefectural Police Headquarters,
Complex Systems Research Lab, Pakistan
COMSATS Institute of IT, Pakistan
COMSATS University Islamabad (CUI), Pakistan
Conestoga College, Canada
COEP Technological University, India
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 Fix Ltd, Pakistan
Cyber Technology Institute De Montfort University, United Kingdom
Cybersecurity Strategic Technology Centre, Singapore
Cyber Silo Inc., United States of America
Czech Technical University in Prague (CTU), Czech

D

Dalhousie University, Canada
Dalian Jiaotong University, China
DAV University, India
Deakin University Australia, Australia
Defence Academy of the United Kingdom, United Kingdom
Delhi Technological University, India
Democritus University of Thrace, Greece
Department of CSE IIT Madra, India
Department of CSIS, BITS Pilani, India
Department of multimedia technologies and telecommunications,
Dr. Babasaheb Ambedekar Marathwada University, Iran
Dr. Shariati Technical and Vocational College, Iran
DS/S Computational Finance Inc., Canada
Dublin city university, Ireland
Durham College, Canada

E

East China University of Science, China
East Delta University, Bangladesh
Eastern Institute of Technology, New Zealand
Edectus.com, Malaysia
EFREI PARIS, France
Electronics and Telecommunications Research Institute (ETRI), Korea
Embry-Riddle Aeronautical University, United States of America
Emory University, United States of America
Enugu State University of Science and Technology, Nigeria
Equinix, United States of America
Erlangen University, Germany
Eskisehir Osmangazi University, Turkey

F

Fachhochschule der Wirtschaft, Germany
Faculdade de CiĂŞncias 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 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
Fitchburg State University, United States of America
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
FUnited Kingdomuoka Institute of Technology, Japan
Fulda University of Applied Sciences, Germany
Fundação Getulio Vargas (FGV), Brazil
Fuzhou University, China

G

G-Info Technology Solutions Pvt. Ltd., India
G H Patel College of Engineering & Technology, India
Gachon University, Seongnam,
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
Google, United States of America
Government College University, Pakistan
Govind Ballabh Pant Institute of Engineering and Technology (GBPID), India
Graphic Deemed University, India
Grand Canyon University, United States of America
Green University of Bangladesh, Bangladesh
Guangdong Pharmaceutical University, China
Guangdong University of Technology, China
Guangxi Key Laboratory of Cryptography and Information Security, China
Guangzhou University, China
Guilin University of Electronic Technology, Iran
Gujarat Technological University, India
Guru Govind Singh University, India

H

Hacettepe University, Turkey
Hadhramout University, Yemen
Hainan Normal University, China
Hangzhou Dianzi University, China
Hanoi University of Science and Technology (HUST), Vietnam 
Haramaya University, Ethiopia
Harbin Institute of Technology, China
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
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 fĂĽr Telekommunikation Leipzig (HfTL), Germany
Hohai University, China
Hokkaido University, Japan
Holy Angel University, Philippines
Humboldt University of Berlin, Germany
Hunan University of Humanities, China
HTI, Egypt

I

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
IILM Univeristy, India
IIT BHU, India
Illinois Institute of Technology, United States of America
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 Pune India, India
Indian Institute of Science, India
Indian Institute of Technology Guwahati, 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
Institut Superieur d'Informatique de Mahdia, Tunisia
Institut supérieur de l'électronique et du numérique, 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
Integer Ltd, india
Intelligent Policing Key Laboratory of Sichuan Province, Sichuan
International Burch University (IBU), Bosnia and Herzegovina
International Hellenic University, Greece
IPN, Mexico
Iqra University - Islamabad Campus (IUIC), Pakistan
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, France
Islamic Azad University, 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, Turkey

J

Jagannath University, Bangladesh
Jadavpur University, India
Jahangirnagar University, Bangladesh
Jiangsu Province Engineering Research Center of Security for Ubiquitous Network, China
Jiangsu University, China
Jinan University, China
JNU, China
John Jay College of Criminal Justice, United States of America
Johns Hopkins University, United States of America
Jordan University of Science & Technology (JUST), Jordan
Jubail Industrial College, Saudi Arabia

K

Kadir Has University, Turkey
Kapadokya Universty, Turkey
Kaplan Business School, Australia
KarabĂĽk 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
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
KNUST, Ghana
Kokurakita Police Station, FUnited Kingdomuoka Prefectural Police, Iapan
Kongu Engineering College, India
Kookmin University, South Korea
Korea National Defense University, South Korea
KPR Institute of Engineering and Technology, India
KTH Royal Institute of Technology, Sweden
Kumoh National Institute of Technology, South Korea
Kütahya Dumlupınar üniversitesi, Turkey 
Kwame Nkrumah University of Science and Technology, Ghana
Kyushu University, Japan

L

La Salle University – Ramon Llull, Spain
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
Le Quy Don Technical University, Vietnam
Lebanese American University (LAU), Lebanon
Lebanese University, Lebanon
Lemon IT, Indonesia
Load Go Transport Inc., Canada
Loughborough University London,
Lovely Professional University, India

M

M.Kumarasamy College of Engineering (MKCE), India Madurai Kamaraj University, 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
Manchester Metropolitan University, United Kingdom
Manipal Institute of Technology Bengaluru, India
Manipal University Jaipur, India
Mansoura University, Egypt
Marathwada University, India
Marmara university, Turkey
Marwadi University, India
Materials and Chemistry Convergence Technology, South Korea
Maven Technical, Nigeria
Medcare MSO, Pakistan
Menoufia University, Egypt
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, Egypt
Mississippi State University, United States of America
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
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
Mumbai University, India
Murdoch University, Australia

N

Najran University, Saudi Arabia
Namal University Mianwali, Pakistan
Nanjing Tech, 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 Ireland, Ireland
National Dong Hwa University, Taiwan
National Forensic Sciences University (NFSU), India
National Institute of Technology (NIT), India
National Institute of Technology Karnataka (NITK), India
National Institute of Technology Tiruchirappalli (NITT), India
National Kaohsiung University of Science and Technology, Taiwan
National Sun Yat-sen University, 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 Technology (NUTECH), Pakistan
National University of Sciences and Technology (NUST), Pakistan
National University Singapore (NUS), Singapore
National Yunlin University of Science and Technology, Taiwan
Necmettin Erbakan University, Turkey
Nehemiah Security Co., United States of America
New Mansoura University, Egypt
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
NIIT University, India
Nile University, Egypt
NIMS University, India
Ninevah University, Iraq
Nirma University, India
NIT Raipur, India
Nirma University, India
Nitte Meenakshi Institute of Technology (NMIT), India
NMAM Institute of Technology, 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, United States of America
Northeastern University, United States of America
North South University, Bangladesh
Northumbria University, United Kingdom
Northwestern Polytechnical University, China
Norwegian University of Science and Technology, Norway
Norwegian University of Science and Technology (NTNU), United States of America
Novasibirsk State University, Russia
NUST School of Electrical Engineering and Computer Science (NUST-SEECS), Pakistan

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
Oracle Lab, Canada
Oriental University, India
Oslo Metropolitan University, Norway

P

Pabna University of Science and Technology, Bangladesh
Pacific Northwest National Laboratory, United States of America
Packetwerk, Germany
Pakistan Institute of Engineering and Applied Sciences, Pakistan
Panimalar, India
Panjab University, Chandigarh, India
Parahyangan catholic university, Indonesia
Patuakhali Science and Technology University, Bangladesh
Penn State University, USA
Pennsylvania State University, USA
Pentangularit, Bangladesh
People's Public Security University of China (PPSUC), china
PES Institute of Technology and Management, India
Petroleum-Gas University of Ploiesti, Romania
PLA Information Engnieering University,Zhengzhou,china
Polish-Japanese Academy of Information Technology, Poland
Politecnico di Milano, Italy
Politeknik Negeri Cilacap, Indonesia
Political Elektronika Negeri Surabaya (PENS), Indonesia
Pölten University of Applied Sciences, Austria
Polytechnic Institute of Leiria (IPLeiria), Portugal
Posts and Telecommunications Institute of Technology (PTIT), Vietnam
Premier University, Bangladesh
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, Purdue University, [Country not added — please clarify]
Purple Mountain Laboratories, China
PUnited States of American National University (PNU), South Korea

Q

Qatar university, Qatar
Quantea.com, USA
Queen’s University Belfast, United Kingdom
Queen mary University of London, United Kingdom
Queens College, United States of America
Queensland University of Technology (QUT), Australia

R

R.V. College of Engineering Bengaluru, India
Radboud University, Netherlands
rAIxon, United Kingdom
Rajarambapu Institute of Technology, India
Rentile, Czech
Reva University, India
Rey Juan Carlos University, Spain
Rice University, United States of America
RMIT University, Australia
Rochester Institute of Technology, United States of America
Royal Military College of Canada, Canada
RUDRA Cybersecurity, India

S

S.E.A Protection Services, Canada
SĂŁo Paulo State University, Brazil
Sabanci University, Turkey
Sacred Heart University (SHU), United States of America
Sadhu Vaswani Institute of Management Studies (SVIMS), India
Sakarya University, Turkey
Sam Houston State University, United States of America
Sama Partners Business Solutions, Tunisia
Samsun University, Turkey
San Jose State University, United States of America
SĂŁo Paulo State University, Brazil
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
SecureITlab, Bahrain
Sejong University, South Korea
SelçUnited Kingdom Üniversitesi, Turkey
Seneca College of Applied Arts and Technology, Canada
Shaanxi Normal University, China
Shahid Bahonar University of Kerman, Iran
Shahid Beheshti 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
Sheffield Hallam University, United Kingdom
Shiraz University, Iran
Siberian Federal University, Russia
Sichuan Normal University, China
Sichuan University, China
Sikkim Manipal Institute of Technology, India
Siliguri Institute of Technology, India
Simon Fraser University, Canada
Singapore Management University (SMU), Singapore
Sistan and Baluchestan Univerisity, Iran
Skyline University College, United Arab Emirates
Slovak University of Technology, Slovakia
SoftCrafted.com, USA
Sol Plaatje University, South Africa
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 Ramaswamy Memorial Institute of Science and Technology (SRM), India
Sri Sivasubramaniya Nadar College of Engineering, India
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. 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
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 Arabia
Taif University, Saudi Arabia
Taiz, 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
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
Thamar University, Yemen
The Alan Turing Institute, United Kingdom
The Maharaja Sayajirao University of Baroda, India
The Open University, Milton Keynes, United Kingdom
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
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 Universityv Toronto Metropolitan University, [Country not clear — please clarify]
Tongji University, China
Trakya University, Turkey
Trideum Corporation, United States of America
Tshimologong Precinct, Technology Park in South Africa
Tsinghua University, China
TU Wien, Austria

U

UAF sub campus burewala, Pakistan
UCAS, Bangladesh
UCL, 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 Cádiz, Spain
Universidad de la Laguna, Spain
Universidad del Cauca, Spain
Universidad Diego Portales, Spain
Universidad Nacional de AsunciĂłn, Spain
Universidad Nacional de EducaciĂłn a Distancia (UNED), Spain Universidad Popular AutĂłnoma del Estado de Puebla (UPAEP), Mexico
Universidad TĂ©cnica Federico Santa MarĂ­a (USM), Chile
Universidade da Coruna, Spain
Universidade federal De lavras, Brazil
Universidade Federal de Santa Catarina (UFSC), Brazil
Universidade Federal do MaranhĂŁo (UFMA), Brazil
Universidade Federal Fluminense, 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
Universita Della Calabria, Italy
Universitaet Klagenfurt, Austria
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 Putra Malaysia, Malaysia
Universiti Sains Malaysia (USM), Malaysia
Universiti Technology Malaysia, 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 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 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 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 of Liberal Arts Bangladesh (ULAB), Bangladesh
University of Liechtenstein
University of Ljubljana, Slovenia
University of London, United Kingdom
University of Luxembourg, Luxembourg
University of Malta, Malta
University of Maryland, United States of America
University of Maryland, United States of America
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 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 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, Indonesia and United Kingdom
University of Swabi, Pakistan
University of Tabriz, Iran
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 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 Western Ontario, Canada
University of Windsor, Canada
University of Wisconsin–Eau Claire, United States of America
University of Wisconsin-Madison, United States of America
University of Wurzburg, Germany
University of ZĂĽrich, Switzerland
Universitat Politecnica de València, Spain
University Saience Malaysia (USM), Malaysia
University Utara Malaysia, Malaysia
UNOB, Slovakia
UNOC, Czech Republic
UNSW Sydney, Australia
UPAEP, Mexico
UPC BarcelonaTech Vilanova, Spain
USP, Brazil
Uwieazy Inc., Indonesia

V

Velagapudi Ramakrishna Siddhartha Engineering College, India
Vellore Institute of Technology (VIT), India
Viavi Solutions, United States of America
Victoria University of Wellington, New Zealand
Vietnam National University, Vietnam
Vietnamese-German University, Vietnam
Vishwakarma University, India

W

Warsaw University of Technology, Poland
Wayne State University, United States of America
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
Women's Christian College, India
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, China
Xidian University, China

Y

YANDEX Co., Russia
YarmoUnited Kingdom University, School of Information Technology and Computer Science, Jordan
YAYAFOOD, Nigeria 
Yeditepe University, Turkey
Yildiz Technical University, Turkey
Yonsei University, South Korea
Yuan Ze University, Taiwan

Z

Zarqa University, Jordan
Zealand - Sjællands Erhvervsakademi, Denmark
Zetech University, Kenya
Zhengzhou University, 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