The series "Studies in Big Data" (SBD) publishes new developments and advances in the various areas of Big Data-quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence incl. neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.
Distributed Denial of Service (DDoS) attacks are a serious threat to Cloud. These attacks consume large amount of resources and increase the service usage cost by a significant factor. Due to multi-tenancy and self-provisioning properties of Cloud, traditional DDoS detection techniques cannot be directly applied. Hence, there is a need for Cloud-specific DDoS detection framework. In this paper, a statistical and distributed network packet filtering model is proposed against DDoS attacks in Cloud. The key idea of this scheme is to distribute multiple packet filters among individual virtual machines, which generate and share collective profile of normal behaviour with a coordinator node at constant intervals. Statistics of selected network attributes construct the normal behaviour profile. Based on the deviation from normal behaviour a decision is made whether to accept or reject the incoming packet. The coordinator node monitors filter and distribute the averaged profile to newly provisioned nodes. Individual profiles have low memory and storage requirements and are updated dynamically. Simulation study indicates the effectiveness of this scheme in detecting DDoS attacks in Cloud.
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