Large-Scale Network Cyberattacks Multiclass Dataset 2024 (LSNM2024)

Published: 1 July 2024| Version 1 | DOI: 10.17632/7pzyfvv9jn.1
Contributors:
Qasem Abu Al-Haija,
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Description

We present a novel cutting-edge, large-scale multiclass dataset to improve the security of network cognition of suspicious traffic in networks. The proposed newly generated dataset contains up-to-date samples and features available to the public to help reduce the effect of upcoming cyberattacks with machine learning methods. Specifically, 6 million traffic samples with 60 features are collected and organized into two balanced classes: 50% normal traffic and 50% anomaly (attack) traffic. Furthermore, the anomaly traffic is composed of 15 different attacks including MITM-ARP-SPOOFING attack, SSH-BRUTE FORCE attack, FTP-BRUTE FORCE attack, DDOS-ICMP, DDOS-RAWIP attack, DDOS-UDP attack, DOS attack, EXPLOITING-FTP attack, FUZZING attack, ICMP FLOOD attack, SYN-FLOOD attack, PORT SCANNING attack, REMOTE CODE EXECUTION attack, SQL INJECTION attack, and XSS attack. For detailed info, Please refer to and cite our article: Q. Abu Al-Haija, Z. Masoud, A. Yasin, K. Alesawi, Y. Alkarnawi, "Revolutionizing Threat Hunting in Communication Networks: Introducing a Cutting-Edge Large-Scale Multiclass Dataset", 15th International Conference on Information and Communication Systems (ICICS 2024), IEEE, Aug. 2024.

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Steps to reproduce

For detailed info, Please refer to and cite our article: Q. Abu Al-Haija, Z. Masoud, A. Yasin, K. Alesawi, Y. Alkarnawi, "Revolutionizing Threat Hunting in Communication Networks: Introducing a Cutting-Edge Large-Scale Multiclass Dataset", 15th International Conference on Information and Communication Systems (ICICS 2024), IEEE, Aug. 2024.

Institutions

Princess Sumaya University for Technology, Jordan University of Science and Technology

Categories

Artificial Intelligence, Cybersecurity, Machine Learning, Intrusion Detection, Forensic Analysis, Defensive Behavior, Intrusion Analysis, Networking, Deep Learning, Cyber Attack, Adversarial Machine Learning

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