Misbehavior Detection Framework for Community-Based Cloud Computing

The success and continuation of cloud computing depends to a large extent on the quality and performance of the offered services. We propose in this paper a novel architecture for cloud computing called Community-based Cloud Computing whose main goal is to improve the quality and performance of the cloud services. In this architecture, cloud services sharing the same domain of interest are partitioned into a set of communities led by a central entity called master. The advantages of such an architecture are (1) facilitating the discovery of cloud services, (2) providing efficient means for better QoS management and resources utilization, and (3) easing intra-layer and cross-layer compositions. However, one of the serious challenges against the success of such an architecture is the presence of malicious services that launch attacks either against the whole community or against some partners in that Community. Therefore, we address this problem by proposing a misbehavior detection framework based on the Support Vector Machine (SVM) learning technique. In this framework, the master of the community monitors the behavior of its community members to populate the training set of the classifier. Thereafter, SVM is used to analyze this set and predict the final classes of the cloud services. Simulation results show that our framework is able to produce highly accurate classifiers, while maximizing the attack detection rate and minimizing the false alarms. They show also that the framework is quite resilient to the increase in the number of malicious services.