CYBERSECURITY DATASETS

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 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.

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.

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, USA

For more information and download this dataset, visit this page.

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.

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 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.

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.

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.

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.

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.

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.


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 thousand 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.


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.


To be sure about the quantity and diversity of this dataset in CIC, 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 in ISCX 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.


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Companies, research centres and universities that have downloaded these datasets:
    A&M College,
    Aalborg University,
    Aalto university,
    Abdullah Gul University (AGU),
    Abu Dhabi University,
    Acharya Nagarjuna university,
    Addis Ababa University,
    Adhiyamaan College of Engineering,
    Advanced Technologies Application Center,
Ain Shams University,
Air University,
Alagappa Government Arts College,
Al-Ameen Engineering College,
Al-Aqsa University,
Al-Baha University,
Albaydha University,
Alexandrian Technological Institute of Thessaloniki,
Al-Huson University College,
Al-Maafer Community College,
American University, Washington,
Amity University Rajasthan,
Amity University,
Amman Arab University,
Amrita School of Engineering (Bengaluru),
Andhra University,
Ankara Yıldırım Beyazıt University,
Anna University
APJ Abdul Kalam Technological University,
Aristotle University of Thessaloniki,
Baden-Wuerttemberg Cooperative State University (DHBW),
Bahria University,
basaheb Bhimrao Ambedkar University,
Beihang University,
Beijing University of Posts and Telecommunications,
Beijing University of Technology,
Beijing University,
Ben-Gurion University of the Negev Beer-Sheva,
Bennett University,
Big Data and Intelligent Systems Laboratory,
Birla Institute of Technology and Science (Pilani),
Boğaziçi University,
Bordeaux university,
Bournemouth University,
Bowie State University,
Brandeis University,
Brno University of Technology,
Burapha University,
California State University (Fullerton),
Canara Engineering College,
Cape Peninsula University of Technology,
Capital University of Science and Technology
Carnegie Mellon University,
Catholic University of the Maule
Center for Cybersecurity, Bruno Kessler Foundation
Central Connecticut State University
Central Connecticut State University,
Central Institute of Technology Kokrajhar
Central University of Finance and Economics
Central University of Haryana
Centre for Information and Communications Technology Research (CITIC)
Centro Universitário da FEI
Chandigarh University,
Charles University,
Cheikh Anta Diop University of Dakar,
Chile university
China Academy of Engineering Physics
China West Normal University
Chinese Academy of Sciences,
Chitkara University
Chongoing University,
Chongqing University of Posts and Telecommunications
Cochin University of Science and Technology
College of Charleston,
College of Engineering Trivandrum
Colorado Mesa University
COMAC Shanghai Aircraft Manufacturing
Community Safety Department, Fukuoka Prefectural Police Headquarters,
COMSATS Institute of IT,
Comsats University
Council for Scientific and Industrial Research (CSIR),
Cranfield University,
CTU in Prague
Cyber Technology Institute De Montfort University
Cybersecurity Strategic Technology Centre
Czech Technical University in Prague (CTU)
Dalhousie University,
DAV University,
Deakin University Australia,
Defence Academy of the UK,
Delhi Technological University,
Democritus University of Thrace,
Department of CSE IIT Madra,
Department of CSIS, BITS Pilani,
Department of multimedia technologies and telecommunications,
Dr. Babasaheb Ambedekar Marathwada University,
Dublin city university,
East China University of Science,
East Delta University,
Electronics and Telecommunications Research Institute (ETRI),
Embry-Riddle Aeronautical University,
FAST National University of Computer and Emerging Science,
Federal University of Juiz de Fora (UFJF),
Federal University of Pará (UFPA),
Federal University of Technology Akure, Federal University of Viçosa (UFV)
Federation University Australia
Florida Atlantic University,
Foundation University School of Science and Technology (FUSST),
Fraunhofer IIS,
Free University Bozen-Bolzano,
Fukuoka Institute of Technology,
Fulda University of Applied Sciences,
G H Patel College of Engineering & Technology,
Gachon University, Seongnam,
Gaston Berger University,
Gazi University,
George Mason University,
George Washington University,
Georgia Tech University,
Ghent University,
Gitam University,
Government College University,
Grand Canyon University,
Guangdong Pharmaceutical University,
Guangdong Province Precise Medicine and Big Data Engineering Technology Research Center for Traditional Chinese Medicine,
Guangdong University of Technology,
Guangxi Key Laboratory of Cryptography and Information Security,
Guangzhou University,
Guilin University of Electronic Technology,
Gujarat Technological University,
                   Hacettepe University,
                   Hainan Normal University,
                   Hangzhou Dianzi University,
                   Hanoi University of Science and Technology,
                   Harbin Institute of Technology,
                   Helwan University,
                   Helwan University,
                   Henan Key Laboratory of Network Cryptography Technology,
                   Henan Police College,
                   Hewlett-Packard,
                   Hillstonenet Co.,
                   Hitech Solutions (Pvt) Ltd.,
                   Hochschule Fulda University of Applied Sciences,
                   Hochschule für Telekommunikation Leipzig (HfTL),
                   Hokkaido University,
                   Humboldt University of Berlin,
                   Hunan University of Humanities,
                   IBM Research,
                   Ibn Tofail University,
                   ICAR-CNR (ITALY),
                   ICT Convergence Research Center,
                   Imperial College London,
                   Independent university of Banja Luka,
                   Indian Institute of Information Technology,
                   Indian Institute of Science,
                   Indian Institute of Technology Guwahati,
                   Industrial Technology Research Institute, Hsinchu, Taiwan
                   Industrial University of Ho Chi Minh city,
                   Innopolis University,
                   Institute for Development and Research in Banking Technology,
                   Institute for Infocomm Research (I2R),
                   Institute for Information and Communication Technologies,
                   Institute of Information Engineering,
                   Institute of Information Engineering Chinese Academy of Sciences Beijing,
                   Instituto Militar de Engenharia,
                   Intelligent Policing Key Laboratory of Sichuan Province, Sichuan
                   International Hellenic University,
                   IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires,
                   Istanbul Technical University,
                   IT-Convergence Engineering,
                   Izmir University of Economics,
                   Jagannath University,
                   Jahangirnagar University,
                   Jiangsu Province Engineering Research Center of Security for Ubiquitous Network,
                   Jiangsu University,
                   Johns Hopkins University,
                   Karabük University,
                   Karunya University,
                   Keldysh Institute of Applied Mathematics, KIAM",
                   Kennesaw State University,
                   Khalifa University,
                   King AbdulAziz University,
                   King Fahad University of Petroleum and Minerals,
                   King Fahad University,
                   King Fahd University of Petroleum & Minerals,
                   King Faisal University,
                   King Khalid University,
                   King Mongkut's Institute of Technology Ladkrabang,
                   King Saud University,
                   Kokurakita Police Station, Fukuoka Prefectural Police
                   Kongu Engineering College,
                   Kookmin University,
                   Korea National Defense University,
                   KPR Institute of Engineering and Technology,
                   KTH Royal Institute of Technology,
                   Kumoh National Institute of Technology,
                   Kyushu University,
                   Laboratory of Numerical Analysis and Computer Science,
                   Lancaster University,
                   Lanzhou University,
                   Lawrence Livermore National Laboratory,
                   Le Quy Don Technical University,
                   Lebanese American University,
                   Lebanese University,
                   Loughborough University London,
                   Loughborough University,
                   Lovely Professional University,
                   Majmaah University,
                   Malaviya National institute of technology,
                   Malta College of Arts,
                   Manchester Metropolitan University
                   Manipal Institute of Technology Bengaluru
                   Manipal University Jaipur,
                   Mansoura University,
                   Marathwada University,
                   Marmara university,
                   Marwadi University,
                   Materials and Chemistry Convergence Technology,
                   Menoufia University,
                   Metropolitan University of Technology,chile
                   Microsoft India Development Center, India
                   Middle East Technical University in Ankara,
                   Mississippi State University,
                   MIT College of Engineering (India),
                   MIT Lincoln Laboratory,
                   MITRE.org,
                   Mizoram University
                   Monash University, Indonesia
                   Morgan State University,
                   Moscow Institute of Physics and Technology,
                   Moscow Institute of Physics and Technology, MIPT
                   Mother Teresa Women’s University, Kodaikanal
                   MSIS Department, Rutgers University
                   Najran University
                   Nanjing University of Information Science and Technology
                   Nanjing University of Science and Technology
                   National Chiao Tung University,
                   National Chung Cheng University,
                   National Dong Hwa University
                   National Institute of Technology (NIT),
                   National Institute of Technology Karnataka
                   National Kaohsiung University of Science and Technology,
                   National Telecom Regulatory Authority (NTRA)
                   National University of Computer and Emerging Sciences, Islamabad, Pakistan
                   National University of Defense Technology (NUDT),
                   National University of Defense Technology Changsha, China
                   National University of Defense Technology, China
                   National University of Sciences and Technology, Islamabad, Pakistan
                   National University Singapore (NUS),
                   National Yunlin University of Science and Technology,
                   Necmettin Erbakan University,
                   NEHEMIAH Security Co.,
                   Nha Trang University
                   Nigeria Federal University of Technology,
                   NIIT University,
                   Nile University
                   Nirma University
                   NIT Raipur
                   Nitte Meenakshi Institute of Technology (NMIT),
                   niversity of Hertfordshire
                   NMAM Institute of Technology,
                   North Carolina A&T State University
                   North Carolina Agricultural and Technical State University,
                   North Dakota State University
                   North South University
                   Northumbria University,
                   Northwestern Polytechnical University
                   Norwegian University of Science and Technology
                   Norwegian University of Science and Technology (NTNU), NOVETTA,
                   Novasibirsk State University,
                   nstitute for Development and Research in Banking Technology (IDRBT)
                   Oak Ridge National Laboratory
                   OAKLAND Univeristy,
                   Old Dominion University
                   Open University of Netherlands,
                   Oracle Lab (Canada),
                   Oslo Metropolitan University
                   Pacific Northwest National Laboratory
                   Packetwerk,
                   Pakistan Institute of Engineering and Applied Sciences
                   Panjab University, Chandigarh
                   Patuakhali Science and Technology University
                   Penn State University,
                   Pennsylvania State University
                   People’s Public Security University of China
                   PES Institute of Technology and Management
                   Petroleum-Gas University of Ploiesti
                   PLA Information Engnieering University,Zhengzhou,china
                   Politecnico di Milano,
                   Political Elektronika Negeri Surabaya (PENS),
                   Pölten University of Applied Sciences
                   Prince Mohammad Bin Fahd University
                   Prince Sultan University,
                   Princess Sarvath Community College
                   Princess Sumaya University for Technology (PSUT)
                   Princeton University,
                   Punjab University, Purdue University,
                   Purple Mountain Laboratories
                   Qatar university
                   Queen’s University Belfast
                   Queensland University of Technology (QUT)
                   R.V. College of Engineering Bengaluru
                   Radboud University
                   RMIT University
                   Rochester Institute of Technology
                   Royal Military College of Canada,
                   Sabanci University,
                   Sakarya University,
                   Sam Houston State University
                   San Jose State University
                   Sathyabama Institute of Science and Technology
                   School of Cyber Security, University of Chinese Academy of Sciences, Beijing
                   Science and Technology (MCAST),
                   Sejong University,
                   Seneca College of Applied Arts and Technology
                   Shahid Bahonar University of Kerman,
                   Shahid Sattari Aviation University
                   Shandong Normal University,
                   Shanghai Jiao Tong University,
                   Shanxi University of Finance and Economics
                   Sharda University
                   Sharif University of Technology,
                   Sheffield Hallam University,
                   Shiraz University,
                   Sichuan Normal University
                   Sichuan University,
                   Sikkim Manipal Inst. of Technology
                   Siliguri Institute of Technology
                   Simon Fraser University
                   Sistan and Baluchestan Univerisity,
                   Skyline University College
                   Slovak University of Technology,
                   Soongsil University,
                   South China University of Technology
                   South East Technological University, Waterford, X91 HE36 Ireland
                   South Ural State University
                   Southeast University, PR China
                   Southern University
                   Southwest Petroleum University
                   SRM Institute of Science and Technology (Deemed to be University)
                   St. Joseph's College of Engineering, Chennai, India
                   St. Polten University of Applied Sciences,
                   State University of Londrina,
                   Strategic Support Force Information Engineering University,
                   Sun Yat-sen University,
                   Sungkyunkwan University,
                   Supercomputer Education and Research Center Supercomputer Education and Research Center
                   Surabaya State Polytechnics,
                   Tai Solarin University of Education,
                   Taiz, Yemen
                   Tallinn University of Technology
                   Tarbiat Modares University
                   Technical University of Dresden,
                   Technical University of Madrid,
                   Technical University of Munich
                   Telecom Bretagne,
                   Telecommunications and Applications Cheikh Anta Diop University
                   Telkon University,
                   Tennessee State University,
                   Texas A&M University Kingsville,
                   The Maharaja Sayajirao University of Baroda
                   The Open University, Milton Keynes
                   The University of Adelaide
                   The University of British Columbia (UBC)
                   The University of Hong Kong
                   The University of Jordan,
                   The University of Manitoba
                   The University of Sydney
                   The University of Tokyo
                   Tianjin University, Tsinghua University,
                   Tokyo Institute of Technology
                   Tomsk Polytechnic Universityv Toronto Metropolitan University
                   Trakya University
                   Umm Al-Qura University
                   United States Military Academy
                   United Technologies Research Center (UTRC),
                   Univerisyt of Twente,
                   Universidad del Cauca,
                   Universidad Diego Portales
                   Universidad Nacional de Asunción,
                   Universidade da Coruna,
                   Universidade Federal Fluminense,
                   Universidade Regional de Blumenau
                   Universita degli Studi di Messina
                   Università degli Studi di Napoli Federico II,
                   Università degli Studi Roma Tre,
                   Universita Della Calabria,
                   Universitaet Klagenfurt
                   Universitas Trunojoyo Madura
                   Université Bretagne Loire,
                   Université de Moncton
                   Université Paris-Saclay
                   Universiti Putra Malaysia,
                   Universiti Sains Malaysia
                   Universiti Sains Malaysia (USM)
                   Universiti Sains Malaysia, Penan
                   Universiti Technology Malaysia,
                   University at Buffalo
                   University Carlos
                   University Cobur of Germany,
                   University College Dublin
                   University Kasdi Merban Ouargla,
                   University Malaysia Pahang,
                   University of Aegean
                   University of Aizu,
                   University of Alcalá de Henares,
                   University of Amsterdam,
                   University of Applied Sciences Wedel,
                   University of Bahrain,
                   University of Bari “Aldo Moro”
                   University of Birmingham Dubai
                   University of Brasília
                   University of Bridgeport,
                   University of Cagliari
                   University of Calgary,
                   University Of Central Punjab
                   University of Chinese Academy of Science
                   University of Cincinnati,
                   University of Colorado
                   University of Dayton,
                   University of Edinburgh
                   University of Electronic Science and Technology of China,
                   University of Essex,
                   University of Fort Hare,
                   University of FuZhou,
                   University of Glasgow,
                   University of Granada,
                   University of Greenwich
                   University of Health Sciences,Istanbul,Turkiye
                   University of Hertfordshire
                   University of Hohenheim
                   University of Houston,
                   University of Hyderabad
                   University of Hyogo,
                   University of Information Technology, Ho Chi Minh city, Vietnam
                   University of Isfahan
                   University of Johannesburg
                   University of Kerala
                   University Of Lahore
                   University of Lay Adventists of Kigali
                   University of Liberal Arts Bangladesh (ULAB)
                   University of Liechtenstein
                   University of Ljubljana,
                   University of London
                   University of Luxembourg,
                   University of Maryland
                   University of Maryland,
                   University of New Haven,
                   University of New South Wales
                   University of North Carolina at Charlotte
                   University of Ontario,
                   University of Oslo
                   University of Ottawa,
                   University of Oviedo,
                   University of Oxford,
                   University of Padua
                   University of Pennsylvania,
                   University of Pretoria
                   University of Salamanca
                   University of Salford
                   University of Sannio
                   University of Santa Cruz Do Sul,
                   University of Science and Technology Beijing,
                   University of Science and Technology of China,
                   University of Sfax
                   University of Southern Queensland,
                   University of St. Andrews,
                   University of Stavanger,
                   University of Stirling
                   University of Strathclyde Glasgow,
                   University of Surabaya University of Surrey,
                   University of Swabi,
                   University of Tabriz,
                   University of Technology of Baghdad,
                   University of Tehran,
                   University of Texas at Dallas (UTDallas),
                   University of Texas at San Antonio
                   University of Trento,
                   University of Twente,
                   University of Washington,
                   University of Waterloo
                   University of West Florida,
                   University of Western Ontario
                   University of Windsor,
                   University of Wisconsin–Eau Claire
                   University of Wisconsin-Madison
                   University of Wurzburg
                   University Utara Malaysia,
                   University, Edmonton
                   University, Islamabad,
                   University, Oshawa
                   UPC BarcelonaTech Vilanova
                   Vellore Institute of Technology,
                   Victoria University of Wellington,
                   Vietnam National University
                   Warsaw University of Technology,
                   Western University
                   Western Washington University (WWU),
                   Widyatama University
                   Wright State University,
                   Wuhan Textile University
                   Wuhan University
                   Xidian University,
                   YANDEX Co.,
                   Yarmouk University, School of Information Technology and Computer Science
                   Yeditepe University,
                   Yildiz Technical University,
                   Yonsei University,
                   Yuan Ze University
                   Zarqa University
                   Zhejiang Gongshang University,
                   Zhejiang University,
                   ZIGHRA Co.
Researchers named among top researchers for Canada 150
The cybersecurity Research and Academic Leadership award, Canada 2019
The cybersecurity academic award, Canada 2017