Loss detection using parameter’s adjustment based on Second Order Self-Similarity statistical model

This paper analyzes loss of self-similarity (LoSS) detection accuracy using parameterpsilas adjustment which includes different values of sampling level and correlation lag. This is important when considering exact and asymptotic self-similar models concurrently in the self-similarity parameter estimation method. Due to the needs of high accuracy and fast estimation, the optimization method (OM) based on second order self-similarity (SOSS) statistical model was proposed in the previous works to estimate self-similarity parameter. Consequently, curve fitting error (CFE) value estimated from OM is used to detect LoSS efficiently. This work investigates the effect of the parameterpsilas adjustment for improving the CFE accuracy and estimation time speed. We have tested the method with real Internet traffics simulation that consists of normal and malicious packets traffic. Our simulation results show that LoSS detection accuracy and estimation time can be affected by the chosen of sampling level and correlation lag values.