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arXiv · 2604.16575

Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic

Yasmin Souza Lima·Rodrigo Moreira·Larissa F. Rodrigues Moreira·Tereza Cristina M. de B. Carvalho·Flávio de Oliveira Silva
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Abstract

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 training, using two diagnostics: lag-1 autocorrelation of an aggregated flow signal and PCA cumulative explained variance. When the probes are inconclusive, the framework reserves a hybrid option as a future fallback rather than an empirically validated branch. Experiments on two statistically distinct datasets with Isolation Forest, One-Class SVM, and KMeans show that structural features consistently match or outperform temporal ones, with the performance gap widening as temporal dependence weakens.

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Yasmin Souza Lima, Rodrigo Moreira, Larissa F. Rodrigues Moreira, Tereza Cristina M. de B. Carvalho, Flávio de Oliveira Silva. 2026-04-17. Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic. https://arxiv.org/abs/2604.16575

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