Modeling of network traffic is a fundamental building block of computer science. Measurements of network traffic demonstrate that self-similarity is one of the basic properties of the network traffic possess at large time-scale. This paper investigates the change of non-stationary self-similarity of network traffic over time,and proposes a method of combining the discrete wavelet transform (DWT) and Schwarz information criterion (SIC) to detect change points of self-similarity in network traffic. The traffic is segmented into pieces around changing points with homogenous characteristics for the Hurst parameter,named local Hurst parameter,and then each piece of network traffic is modeled using fractional Gaussian noise (FGN) model with the local Hurst parameter. The presented experimental performance on data set from the Internet Traffic Archive (ITA) demonstrates that the method is more accurate in describing the non-stationary self-similarity of network traffic.
Trust is one of the most important security requirements in the design and implementation of peer-to-peer (P2P) systems. In an environment where peers' identity privacy is important, it may conflict with trustworthiness that is based on the knowledge related to the peer's identity, while identity privacy is usually achieved by hiding such knowledge. A trust model based on trusted computing (TC) technology was proposed to enhance the identity privacy of peers during the trustworthiness evaluation process between peers from different groups. The simulation results show that, the model can be implemented in an efficient way, and when the degree of anonymity within group (DAWG) is up to 0.6 and the percentage of malicious peers is up to 70%, the service selection failure rate is less than 0.15.
