A Performance Analysis of Intrusion Detection with Snort and Security Information Management
Abstract: Network intrusion detection systems (NIDSs) are a major component in cybersecurity and can be implemented with open-source software. Active communities and researchers continue to improve projects and rulesets used for detecting threats to keep up with the rapid development of the internet. With the combination of security information management, automated threat detection updates and widely used software, the NIDS security can be maximized. However, it is not clear how different combinations of software and basic settings affect network performance. The main purpose in this thesis was to find out how multithreading, standard ruleset configurations and near real-time data shipping affect Snort IDS’ online and offline performance. Investigations and results were designed to guide researchers or companies to enable maximum security with minimum impact on connectivity. Software used in performance testing was limited to Snort 184.108.40.206-WIN64 (IDS), Snort 220.127.116.11 (IDS), PulledPork (rule management) and Open Distro for Elasticsearch (information management). To increase the replicability of this study, the experimentation method was used, and network traffic generation was limited to 1.0 Gbit/s hardware. Offline performance was tested with traffic recorded from a webserver during February 2021 to increase the validity of test results, but detection of attacks was not the focus. Through experimentation it was found that multithreading enabled 68-74% less runtime for offline analysis on an octa-thread system. On the same system, Snort’s drop rate was reduced from 9.0% to 1.1% by configuring multiple packet threads for 1.0 Gbit/s traffic. Secondly, Snort Community and Proofpoint ET Open rulesets showed approximately 1% and 31% dropped packets, respectively. Finally, enabling data shipping services to integrate Snort with Open Distro for Elasticsearch (ODFE) did not have any negative impact on throughput, network delay or Snort’s drop rate. However, the usability of ODFE needs further investigation. In conclusion, Snort 3 multithreading enabled major performance benefits but not all open-source rules were available. In future work, the shared security information management solution could be expanded to include multiple Snort sensors, triggers, alerting (email) and suggested actions for detected threats.
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