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arXiv · 2609.11432

AoI-driven Anomaly Detection Framework for Communication Networks

Zia Ul Islam Nasir·Alper Kose·Berna Ozbek
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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.

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Zia Ul Islam Nasir, Alper Kose, Berna Ozbek. 2026-09-10. AoI-driven Anomaly Detection Framework for Communication Networks. https://arxiv.org/abs/2609.11432

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