Distributed denial of service (DDoS) attacks often use botnets to generate a high volume of packets and adopt controlled zombies for flooding a victim’s network over the Internet. Analysing the multiple sources of DDoS attacks typically involves reconstructing attack paths between the victim and attackers by using Internet protocol traceback (IPTBK) schemes. In general, traditional route-searching algorithms, such as particle swarm optimisation (PSO), have a high convergence speed for IPTBK, but easily fall into the local optima. This paper proposes an IPTBK analysis scheme for multimodal optimisation problems by applying a revised locust swarm optimisation (LSO) algorithm to the reconstructed attack path in order to identify the most probable attack paths. For evaluating the effectiveness of the DDoS control centres, networks with a topology size of 32 and 64 nodes were simulated using the ns-3 tool. The average accuracy of the LS-PSO algorithm reached 97.06 for the effects of dynamic traffic in two experimental networks (number of nodes = 32 and 64). Compared with traditional PSO algorithms, the revised LSO algorithm exhibited a superior searching performance in multimodal optimisation problems and increased the accuracy in traceability analysis for IPTBK problems.
In this paper, we present an approach to characterize and model microblog traffic in cellular data network. In contrast to previous methods, our approach is based on the cloud computing platform and the cluster system, including the Hadoop Distributed File System (HDFS) and the parallel processing software framework MapReduce. Whats more, we focus on the contrast of Sina and Tencent microblogs. We analyze the features of microblog traffic in four aspects of increasing details, which are (i) traffic diurnal pattern, (ii) modeling the traffic distribution, (iii) user distribution, (iv) diversity usage of microblogs. This approach of analyzing microblog traffic comprehensively is probably the most important contribution of this paper. Furthermore, our approach has two important features. First, the massive mobile subscriber data we used in our experiments was collected from a commercial Internet Service Provider (ISP) covering an entire province in Southern China. Therefore, it ensures the results indicate the true characteristics of microblog traffic in network. Second, we investigate that the microblog traffic fits with the power law distribution. We demonstrate the electiveness of our approach on three real datasets. Our results are important for cellular network operators to learn user behavior and optimize the future microblog application designs.
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