Feature Selection for Network Intrusion Detection
Published in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025
Abstract
The paper presents FSNID (Feature Selection for Network Intrusion Detection), a novel information-theoretic method that facilitates the exclusion of non-informative features when detecting network intrusions. Through experiments, the authors demonstrate that the method selects a significantly reduced feature set, while maintaining NID performance.
Code: https://github.com/c-s-westphal/FSNID
Recommended citation: Westphal, C., Hailes, S., & Musolesi, M. (2025). Feature Selection for Network Intrusion Detection. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1599-1610).
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