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 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.