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Cybersecurity Datasets

As part of our Understanding Cybersecurity Series (UCS) knowledge mobilization program, we generate and release cybersecurity datasets to support students, researchers and developers to analyze, test and evaluate their cybersecurity models, solutions and tools.

6. Malicious DNS and Attacks (BCCC-CIC-Bell-DNS-2024)

Using ALFlowLyzer, we successfully generated a new 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:

"Unveiling Malicious DNS Behavior Profiling and Generating Benchmark Dataset through Application Layer Traffic Analysis", Shafi, MohammadMoein, Arash Habibi Lashkari, Hardhik Mohanty; Computers and Electrical Engineering, 2024

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5. Cloud DDoS Attacks (BCCC-cPacket-Cloud-DDoS-2024)

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:

"Toward Generating a New Cloud-Based Distributed Denial of Service (DDoS) Dataset and Cloud Intrusion Traffic Characterization", Shafi, MohammadMoein, Arash Habibi Lashkari, Vicente Rodriguez, and Ron Nevo.; Information 15, no. 4: 195. https://doi.org/10.3390/info15040195

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4. DNS over HTTPS ( BCCC-CIRA-CIC-DoHBrw-2020 )

The 'BCCC-CIRA-CIC-DoHBrw-2020' dataset was created to address the imbalance in the 'CIRA-CIC-DoBre-2020' dataset. Unlike the 'CIRA-CIC-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 'BCCC-CIRA-CIC-DoHBrw-2020' dataset comprises three CSV files: one for malicious DoH traffic, one for benign DoH traffic, and a third that combines both types. 

The full research paper outlining the details of the dataset and its underlying principles:

“Unveiling DoH Tunnel: Toward Generating a Balanced DoH EncryptedTraffic Dataset and Profiling malicious Behaviour using InherentlyInterpretable Machine Learning“, Sepideh Niktabe, Arash Habibi Lashkari, Arousha Haghighian Roudsari, Peer-to-Peer Networking and Applications, Vol. 17, 2023

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3. Vulnerable Smart Contracts (BCCC-VulSCs-2023)

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:

“Unveiling Vulnerable Smart Contracts: Toward Profiling Vulnerable Smart Contracts using Genetic Algorithm and Generating Benchmark Dataset”, Sepideh Hajihosseinkhani, Arash Habibi Lashkari, Ali Mizani, Blockchain: Research and Applications, Vol. 4, 2023

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2. SQL Injection Attack (BCCC-SFU-SQLInj-2023)

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:

"An Evolutionary Algorithm for Adversarial SQL Injection Attack Generation", Maryam Issakhani, Mufeng Huang, Mohammad A. Tayebi, Arash Habibi Lashkari, IEEE Intelligence and Security Informatics (ISI2023), NC, USA

1. Source Code Authorship Attribution (YU-SCAA-2022)

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, it has 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 1,632 code files from 204 authors.

The full research paper outlining the details of the dataset and its underlying principles:

”AuthAttLyzer: A Robust defensive distillation-based Authorship Attribution framework”, Abhishek Chopra , Nikhill Vombatkere , Arash Habibi Lashkari, The 12th International Conference on Communication and Network Security (ICCNS), 2022, China

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Who has made use of our cybersecurity datasets (since September 2023)?

A

  • Abudhabi University, United Arab Emirates 
  • Air University, Pakistan  
  • Amrita Vishwa Vidyapeetham University, India 
  • Australian Department of Defence, Australia 

B

  • Beijing Jiaotong University, China 
  • BlueLotus, Columbia 

C

  • Catholic UNiversity of the Sacred Heart, Italy 
  • Central SOuth University, China 
  • Christ Academy college for Advanced studies, India 
  • Cisco Systems Inc, USA 
  • CodePath, USA 
  • CQVista Inc., South Korea 
  • CUAB, Nigeria 
  • Cyber, Belgium 
  • Cyber Silo Inc., USA  

D

  • Dalhousie University, canada   
  • Dalian Jiaotong University, China 
  • Dr. Shariati Technical and Vocational College, Iran 

E

  • Eastern Institutue of Technology, New Zealand 
  • Erlangen University, Germany 

F

  • Fachhochschule der Wirtschaft, Germany 
  • Fuzhou University, China

G

  • Ghent University, IDLab-Imec, Belgium 
  • Guru Govind Singh University, India 

H

  • Hohai University, China   
  • HUST, Vietnam 

I

  • IIT BHU, India 
  • Indian Institute of Technology (BHU) Varanasi, India 
  • IMP Group International, Canada 
  • Institute of Technology of Cambodia, Cambodia  
  • Islamic Azad University, Iran 
  • Istanbul Technical University, Türkiye 

J

  • Jiangsu University, China 
  • JNU, China 

K

  • Kadir Has University, Türkiye 
  • KMUTNB, Thailand 
  • KNUST, Ghana 

L

  • La Salle University - Ramon Llull, Spain 
  • Load Go Transport Inc, Canada 

M

  • Makerere University, Uganda 
  • Maven Technical, Nigeria 
  • Misr International University, Egypt 
  • Mumbai University, India  

N

  • Nanjing Tech, China  
  • Nanjing University of Science and Technology (NJUST), China  
  • Nanjing University, China  
  • National Forensic Sciences University (NFSU), India  
  • National Forensic Sciences University, India  
  • National Institute of Technology Karnataka, India  
  • National Institute of Technology(NIT), India  
  • National Sun Yat-sen University, Taiwan  
  • National Taiwan University of Science and Technology, Taiwan  
  • National Taiwan University, Taiwan  
  • National University of Technology (NUTECH), Pakistan  
  • New Mansoura University, Egypt  
  • North Carolina State University, USA  
  • Northeastern University, USA 

O

P

  • parahyangan catholic university, Indonesia  
  • Premier University, Bangladesh  
  • Princess Nourah Bint Abdul Rahman University, Saudi Arabia  
  • PSL Research University, France  
  • Pusan National University, South Korea 

Q

R

  • Reva University, India    
  • Rochester Institute of Technology, USA   
  • RUDRA Cybersecurity, India 

S

  • S.E.A Protection Services, Canada  
  • Sacred Heart University, USA  
  • Sardar Patel Institute of Technology, India  
  • São Paulo State University, Brazil 
  • SecureITlab, Bahrain  
  • Selçuk Üniversitesi, Turkey  
  • Seneca college, Canada  
  • Siberian Federal University, Siberia  
  • Singapore Management University (SMU), Singapore   
  • South China University of Technology, China  
  • Southeast University, China  
  • Sri Krishna Arts and Science College, India  
  • Sri Ramaswamy Memorial Institute of Science and Technology (SRM), India   
  • SRM Institute of Science and Technology, India  
  • State Polytechnic of Ujung Pandang, Indonesia  
  • Szkoła Główna Gospodarstwa Wiejskiego w Warszawie, Poland  

T

  • Taibah University, Saudi Arabia  
  • Technical and Vocational University. Shariaty Technical College, Iran  
  • Technological University of Panama, Panama  
  • Telkom University, Indonesia  
  • Th!rd Sarl., Cameroon  
  • Tianjin University of Technology, China  
  • Tongji University, China   
  • Tshimologong Precinct, Technology Park in South Africa  
  • TU Wien, Austria 

U

  • Ulster University, United Kingdom  
  • UNB Canadian Institute For Cybersecurity, Canada  
  • Universidad Popular Autónoma del Estado de Puebla, Mexico  
  • Universidad Popular Autónoma del Estado de Puebla, Mexico  
  • Universiti Tenaga Nasional Malaysia, Malaysia  
  • University Mohammed 5 Rabat, Morocco  
  • University of Calgary, Canada  
  • University of Campania, Italy  
  • University of Chinese Academy of Sciences (UCAS), China  
  • University of Chinese Academy of Sciences, China  
  • University of Colombo, Colombia   
  • University of Delhi, India  
  • University of Delhi, India  
  • University of Eletronic Science and Technology of China, China  
  • University of Engineering and Technology (UET), Taxila, Pakistan  
  • University of Exeter, United Kingdom  
  • University of Guelph, Canada  
  • University of Houston, USA  
  • University of Kashmir, Iraq  
  • University of Kashmir, Pakistan   
  • University of Kelaniya, Sri Lanka  
  • University of Kufa, Iraq  
  • University of Kufa, Iraq  
  • University of New Mexico (UNM), Mexico   
  • University of New Mexico, USA  
  • University of Sao Paulo, Brazil  
  • University of São Paulo, Brazil  
  • University of Science and Technology Chittagong, Bangladesh  
  • University Of Waterloo, Canada  
  • University of Zürich, Switzerland  
  • UNSW Sydney, Australia 

V

Vellore Institute of Technology (VIT), India  

W

  • WHU  
  • Wuhan university, China  

X

 Xi'an University of Technology, China  

Y

York university, Canada 

Z

Zhejiang Universit, China