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cs.CR2026
Improving Network Anomaly Detection via Choquet-Integral-Based Feature Aggregation
Abreu Quevedo, Roger Immich, Giancarlo Lucca +2
This work investigates a generalized Choquet-integral-based feature aggregation framework to improve anomaly detection in high-dimensional network traffic data. The approach combin…
cs.CR2026
On the Impact of Entropy-based Features
Iuri Mundstock, Abreu Quevedo, Jéferson Campos Nobre +3
Network anomaly detection is increasingly challenging due to the growing diversity and variability of traffic patterns, which are not always well captured by traditional statistica…