Rule Generalisation in Intrusion Detection Systems using Snort

Aickelin, Uwe; Twycross, Jamie; Hesketh-Roberts, Thomas

Abstract

Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more necessary as reliance on Internet services increases and systems with sensitive data are more commonly open to Internet access. An IDS’s responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this activity. The majority of IDSs use a set of signatures that define what suspicious traffic is, and Snort is one popular and actively developing open-source IDS that uses such a set of signatures known as Snort rules. Our aim is to identify a way in which Snort could be developed further by generalising rules to identify novel attacks. In particular, we attempted to relax and vary the conditions and parameters of current Snort rules, using a similar approach to classic rule learning operators such as generalisation and specialisation. We demonstrate the effectiveness of our approach through experiments with standard datasets and show that we are able to detect previously undetected variants of various attacks. We conclude by discussing the general effectiveness and appropriateness of generalisation in Snort based IDS rule processing.

Keywords: anomaly detection, intrusion detection, Snort, Snort rules

Citation

Aickelin, U., Twycross, J., & Hesketh-Roberts, T. (2007). Rule Generalisation in Intrusion Detection Systems using Snort. International Journal of Electronic Security and Digital Forensics, 1(1), 101-116. https://doi.org/10.1504/IJESDF.2007.013596

• Journal Article Type: Article
• Online Publication Date: May 10, 2007
• Publication Date: 2007-05
• Deposit Date: Oct 26, 2007
• Publicly Available Date: Oct 26, 2007
• Journal: International Journal of Electronic Security and Digital Forensics
• Print ISSN: 1751-911X
• Electronic ISSN: 1751-9128
• Publisher: Inderscience
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Issue: 1
• Pages: 101-116
• DOI: https://doi.org/10.1504/IJESDF.2007.013596
• Public URL: https://nottingham-repository.worktribe.com/output/1017142
• Publisher URL: https://www.inderscienceonline.com/doi/abs/10.1504/IJESDF.2007.013596