Feature Selection for Network Intrusion Detection
Published in SIGKDD 2025, 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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