Biblio

Many customers ranked cloud security as a major challenge that threaten their work and reduces their trust on cloud service's provider. Hence, a significant improvement is required to establish better adaptations of security measures that suit recent technologies and especially distributed architectures. Considering the meaningful recorded data in cloud generated log files, making analysis on them, mines insightful value about hacker's activities. It identifies malicious user behaviors and predicts new suspected events. Not only that, but centralizing log files, prevents insiders from causing damage to system. In this paper, we proposed to take away sensitive log files into a single server provider and combining both MapReduce programming and k-means on the same algorithm to cluster observed events into classes having similar features. To label unknown user behaviors and predict new suspected activities this approach considers cosine distances and deviation metrics.