Network Anomaly Detection Based on the Statistical Self-similarity Factor

Abstract

The actual network traffic is self-similar or long-range dependent. Self-similarity analysis and anomaly detection in networks are interesting field of research and scientific work of scientists around the world. Simulation studies have demonstrated that the Hurst parameter estimation can be used to detect traffic anomaly—the Hurst values are compared with confidence intervals of normal values to detect anomaly in few kinds of traffic: TCP, UDP. The dramatic expansion of applications on modern networks gives rise to a fundamental challenge to network security. Therefore, it is important to reduce the burstiness for better network performance, and thereby detect traffic anomalies.