Diverse layers of defence play an important role in the design of defence-in-depth architectures. The use of Intrusion Detection Systems (IDSs) are ubiquitous in this design. But the selection of the “right” IDSs in various configurations is an important decision that the security architects need to make. Additionally, the ability of these IDSs to adapt to the evolving threat-landscape also needs to be investigated. To help with these decisions, we need rigorous quantitative analysis. In this paper, we present a diversity analysis of open-source IDSs, Snort and Suricata, to help security architects tune/deploy these IDSs. We analyse two types of diversities in these IDSs; configurational diversity and functional diversity. In the configurational diversity analysis, we investigate the diversity in the sets of rules and the Blacklisted IP Addresses (BIPAs) these IDSs use in their configurations. The functional diversity analysis investigates the differences in alerting behaviours of these IDSs when they analyse real network traffic, and how these differences evolve. The configurational diversity experiment utilises snapshots of the rules and BIPAs collected over a period of 5 months, from May to October 2017. The snapshots have been collected for three different off-the-shelf default configurations of the Snort IDS and the Emerging Threats (ET) configuration of the Suricata IDS. The functional diversity investigates the alerting behaviour of these two IDSs for a sample of the real network traffic collected in the same time window. Analysing the differences in these systems allows us to get insights into where the diversity in the behaviour of these systems comes from, how does it evolve and whether this has any effect on the alerting behaviour of these IDSs. This analysis gives insight to security architects on how they can combine and layer these systems in a defence-in-depth deployment.
We present an analysis of the diversity that exists in the rules and blacklisted IP addresses of the Snort and Suricata Intrusion Detection Systems (IDSs). We analysed the evolution of the rulesets and blacklisted IP addresses of these two IDSs over a 5month period between May and October 2017. We used three different off-the-shelf default configurations of the Snort IDS and the Emerging Threats (ET) configuration of the Suricata IDS. Analysing the differences in these systems allows us to get insights on where the diversity in the behaviour of these systems comes from and how does it evolve over time. This gives insight to Security architects on how they can combine and layer these systems in a defence-in-depth deployment. To the best of our knowledge a similar experiment has not been performed before. We will also show results on the observed diversity in behaviour of these systems, when they analysed the network data of the DMZ network of City, University of London.
The signature-based network Intrusion Detection Systems (IDSs) entails relying on a pre-established signatures and IP addresses that are frequently updated to keep up with the rapidly evolving threat landscape. To effectively evaluate the efficacy of these updates, a comprehensive, long-term assessment of the IDSs' performance is required. This article presents a perspective-retrospective analysis of the Snort and Suricata IDSs using rules that were collected over a four-year period. The study examines how these IDSs perform when monitoring malicious traffic using rules from the past, as well as how they behave when monitoring the same traffic using updated rules in the future. To accomplish this, a set of Snort Subscribed and Suricata Emerging Threats rules were collected from 2017 to 2020, and a labelled PCAP data from 2017-2018 was analysed using past and future rules relative to the PCAP date. In addition to exploring the evolution of Snort and Suricata IDSs, the study also analyses the functional diversity that exists between these IDSs. By examining the evolutionary behaviour of signature-based IDSs and their diverse configurations, the research provides valuable insights into how their performance can be impacted. These insights can aid security architects in combining and layering IDSs in a defence-in-depth deployment.
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