AoI-driven Anomaly Detection Framework for Communication Networks

Abstract

Anomaly detection in communication networks remains a challenge, particularly in time-sensitive environments where stale information can affect system reliability. In this work, we propose an anomaly detection framework that leverages Age of Information (AoI) as a temporal indicator for identifying abnormal network behaviors. The AoI has been studied for network performance optimization, however, its applicability to anomaly detection remains unexplored. The proposed framework integrates a feature selection technique based on the estimated Peak Age of Information (ePAoI) with multiple machine learning models for communication networks as a complementary indicator. The evaluation results demonstrate that ePAoI-based feature selection enhances the performance of the anomaly detection framework while significantly reducing the computational complexity.