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Multi-Step Cyber-Attack Dataset (MSCAD for Intrusion Detection)
- Citation Author(s):
- Submitted by:
- Mohammad Almseidin
- Last updated:
- Sat, 06/18/2022 - 15:58
- DOI:
- 10.21227/phr0-e264
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Abstract
Nowadays, with the rapid increase in the number of applications and networks, the number of cyber multi-step attacks has been increasing exponentially. Thus, the need for a reliable and acceptable Intrusion Detection System (IDS) solution is becoming urgent to protect the networks and devices. However, implementing a robust IDS needs a reliable and up-to-date dataset in order to capture the behaviors of the new types of attacks, especially multi-step attacks. In this work, a new benchmark Multi-Step Cyber-Attack Dataset (MSCAD) is introduced. MSCAD includes two multi-step scenarios; the first scenario is a password cracking attack, and the second attack scenario is a volume-based Distributed Denial of Service (DDoS) attack. The MSCAD was assessed in two manners; firstly, the MSCAD was used to train IDS. Then, the performance of IDS was evaluated in terms of G-mean and Area Under Curve (AUC). Secondly, the MSCAD was compared with other free open-source and public datasets based on the latest key criteria of a dataset evaluation framework. The results show that IDS-based MSCAD achieved the best performance with G-mean of 0.83 and obtained good accuracy to detect the attacks. Besides, the MSCAD successfully passed twelve key criteria.
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