Renyi entropy-driven network traffic anomaly detection with dynamic threshold

Network traffic anomaly detection is a critical issue in network security. Existing Abnormal traffic detection methods rely on statistical-based or anomaly-based approaches, and these detection methods all require a full understanding of traffic characteristics and attack patterns. Information entropy has been widely studied in abnormal traffic detection because it can describe the distribution characteristics of network traffic. However, this method makes it difficult to cope with the timing and variability of network traffic. To address these challenges, this paper proposes a network traffic anomaly detection method based on Renyi entropy. Simultaneously, we introduce a fixed time window and utilize an improved EWMA model within this window to dynamically set thresholds for anomaly detection. Experimental results show that the method proposed in this paper is superior to popular abnormal traffic detection methods in terms of effectiveness and efficiency, it is better adapted to the dynamic changes of network traffic and provides a more reliable solution for anomaly detection.