Large-Scale Network Cyberattacks Multiclass Dataset 2024 (LSNM2024)
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 the MITM-ARP-SPOOFING attack, SSH-BRUTE FORCE attack, FTP-BRUTE FORCE attack, DDOS-ICMP attack, 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 more information about the dataset, please read and cite our research articles: Q. A. Al-Haija, Z. Masoud, A. Yasin, K. Alesawi and Y. Alkarnawi, "Revolutionizing Threat Hunting in Communication Networks: Introducing a Cutting-Edge Large-Scale Multiclass Dataset," 2024 15th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan, 2024, pp. 1-5, doi: 10.1109/ICICS63486.2024.10638287. (https://ieeexplore.ieee.org/document/10638287) Q. A. Al-Haija, Z. Masoud, A. Yasin, K. Alesawi, and Y. Alkarnawi. End-to-End Threat Hunting with a Novel Multiclass Dataset for Intelligent Intrusion Detection. TechRxiv. August 07, 2025. DOI: 10.36227/techrxiv.175459955.56010835/v1 (https://doi.org/10.36227/techrxiv.175459955.56010835/v1)
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For more information about the dataset, please read and cite our research articles: Q. A. Al-Haija, Z. Masoud, A. Yasin, K. Alesawi and Y. Alkarnawi, "Revolutionizing Threat Hunting in Communication Networks: Introducing a Cutting-Edge Large-Scale Multiclass Dataset," 2024 15th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan, 2024, pp. 1-5, doi: 10.1109/ICICS63486.2024.10638287. (https://ieeexplore.ieee.org/document/10638287) Cite as: Qasem Abu Al-Haija, Zaid Masoud, Assim Yasin, et al. End-to-End Threat Hunting with a Novel Multiclass Dataset for Intelligent Intrusion Detection. TechRxiv. August 07, 2025. DOI: 10.36227/techrxiv.175459955.56010835/v1 (https://doi.org/10.36227/techrxiv.175459955.56010835/v1)