Continuous LoSS detection using iterative window based on SOSS model and MLS approach

Abstract

This paper proposes a continuous Loss of Self-Similarity (LoSS) detection using iterative window and Multi-Level Sampling (MLS) approach. The method defines LoSS based on Second Order Self-Similarity (SOSS) statistical model. The Optimization Method (OM) is used to estimate self-similarity parameter since it is fast and more accurate in comparison with other estimation methods known in the literature. The probability of LoSS detection is introduced to measure continuous LoSS detection performance. The proposed method has been tested with real Internet traffic simulation dataset. The results demonstrate that normal traces have probability of LoSS detection below the threshold at all sampling levels. Meanwhile, abnormal traces have probability of LoSS that imitates normal behavior at sampling levels below 100ms but exceeds the threshold at sampling levels larger than 100ms. Our results show the possibility of detecting anomaly traffic behavior based on obtaining continuous LoSS detection monitoring.