On the Impact of Entropy-based Features
Network anomaly detection is increasingly challenging due to the growing diversity and variability of traffic patterns, which are not always well captured by traditional statistical features. In this work, we explore the use of entropy as an additional feature to support supervised network traffic classification. The main idea is to use entropy to represent variability in selected traffic attributes, complementing conventional descriptors rather than replacing them. We integrate the entropy-based feature into a standard machine learning pipeline and evaluate its impact through a direct comparison between models trained with and without this feature. Experiments conducted on a public intru...
| Authors | Iuri Mundstock, Abreu Quevedo, Jéferson Campos Nobre et al. |
|---|---|
| Primary category | cs.CR |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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