Solution Of Large Sparse Matrix In Network Research And Influence Of Traffic Model On Worm Tracing

Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, research citation network analysis, and computation of PageRank. Considering the web is too huge, computing such sparse graphs directly may not be feasible in pratice. This text firstly presented a parallel implementation of large-scale spare matrix algorithm to compute PageRank vector of large network graph. Then we analyzed the performance of this algorithm using convergence experiments and analyzed the influence of the parameter. Then we proposed parameters should be greater than 0.85 when iteration convergence. Finally, the performance of parallel comparison method is compared with the other four different methods, the contrast shows that parallel iterative algorithm with high accuracy and stability, and time and space consuming influenced by the parameters with little impact.Network worms have been a serious security threat on the Internet. Tracing worm propagation path can identify the overall structure of a worm attack's propagation. To detect and defense large scale internet worms, setting up a convenient and safe experimental environment that capable of running and observing real world worm become an important work, it can be a large scale worm test bed for forensic evidence. This paper provides a systemic analysis of large-scale worm propagation tracing experiment strategy which is based on virtual machine technology by setting up an experimental environment called dummy (DU). First, the framework of DU is addressed. Then, it gives the design and control of DU. Finally, DU is analyzed with experiments, and some conclusions are drawn from the experiment results. Experimental results show that DU can trigger large-scale worm outbreaks within the controllable scope of human, observe propagation process of the worm, experiment detection and defense techniques, discover worm propagation characteristic such as scanning method and propagation process, real-time collect network traffic and propagation process, investigate network traffic, dynamically throw out the result, launch speculate algorithm for reconstructing out propagation path of the worm. Then actual worm propagation process can be captured and compared with the results using tracing algorithm.Worms have been a serious security threat on the Internet. They can spread across the Internet quickly with terrible influence, and Internet worms have been a primary issue faced by malicious code researchers. Currently, research on worm detection and containment continuously improved, tracing the evolution of a worm outbreak (attacking path of worm) is an important research area, it not only reconstruct patient zero (i.e., the initial victim), but also the infection node list in evolution process. The reconstruction result has significance in restraining evolution of worm and forensic evidence.Tracing worm at the early stage is helpful to the detection and containment. As Internet traffic usually follows the power-law Poisson model, researches on the worm tracing algorithm for power-law Poisson model is more essential. Based on the hypothesis testing and model parameter computation, we present the k accumulation algorithm for reconstructing worm propagation path under power-law Poisson model, then prove and verify its accuracy through simulation experiments. Experiment results indicate that our algorithm has a significant accuracy.This article firstly presented a parallel implementation of large-scale spare matrix algorithm to computing PageRank vector of large network graph. And then we provide a systemic analysis of large-scale worm propagation tracing experiment strategy which is based on virtual machine technology by setting up an experimental environment called DU. In the rear of this paper, we present the k accumulation algorithm for reconstructing worm propagation path under power-law Poisson model.