Rule Generalisation in Intrusion Detection Systems using Snort

TL;DR

This paper explores enhancing Snort-based intrusion detection systems by generalizing rules to detect novel attacks, demonstrating improved detection of attack variants through experimental validation.

Contribution

It introduces a method for rule generalization in Snort IDS, enabling detection of previously unseen attack variants, which is a novel approach in rule-based intrusion detection.

Findings

Effective detection of attack variants using generalized rules

Improved detection rates demonstrated on standard datasets

Discussion on the applicability of rule generalization in IDS

Abstract

Intrusion Detection Systems (ids)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 responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this activity. The majority of ids 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 undeleted variants of various attacks. We conclude by discussing the general effectiveness and appropriateness of generalisation in Snort based ids rule processing.