Entropy Change Rate for Traffic Anomaly Detection
Xiaowei Li, Changda Wang, An Tang · 2021
Traffic anomaly detection is a key research topic for large scale communication networks. Traditional network entropy has been proved to be an effective metric on network traffic anomaly detection. However, such a method also shows limitations in large scale networks with constantly changing packet flows, which makes the traditional entropy based method inefficient for traffic anomaly detection. To address this problem, we propose a novel indicator named Entropy Change Rate to improve the effectiveness of the traditional entropy based network traffic anomaly detection.