에너지 특성 기반 통신 네트워크 신호 데이터의 이상진단

Abstract

Purpose: With the rapid development of wireless communication, the occurrence of anomalies resulting from malicious network attacks and system overload is also increasing rapidly. Consequently, detecting network traffic anomalies in a network system has become crucial for preventing server downtime. In this study, we proposed a method for detecting anomalies in network traffic data through signal processing and statistical tests. Methods: Based on self-similar characteristics of network traffic data, we employed fractional Brownian motion to extract the Hurst exponent as the health index of network traffic data. Additionally, we proposed the index-based change-point monitoring scheme to assess the network’s current status. Results: Analysis of actual network traffic data shows that the method based on the Hurst exponent and change-point estimation can effectively detect anomalies early, prior to real traffic outbreak, preventing network traffic failures. Conclusion: This research introduced a method for assessing abnormalities and detecting change-point in network overload based on the statistical property of long-range dependency, facilitating early detection of network issues.