3 papers
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…
cs.LG2026
Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier
Arthur G. Bubolz, Abreu Quevedo, Giancarlo Lucca +3
The growing use of Bitcoin as a decentralized digital asset and investment tool has sparked strong interest in understanding its market behavior. This study presents a new approach…