AI RESEARCH
Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic
arXiv CS.LG
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ArXi:2604.16575v1 Announce Type: new Unsupervised anomaly detection is widely used to detect Distributed Denial-of-Service (DDoS) attacks in cloud-native 5G networks, yet most studies assume a fixed traffic representation, either temporal or structural, without validating which feature space best matches the data. We propose a lightweight decision framework that prioritizes temporal or structural features before