Visible to the public Enhancing Cloud Security Using Advanced MapReduce K-means on Log Files

TitleEnhancing Cloud Security Using Advanced MapReduce K-means on Log Files
Publication TypeConference Paper
Year of Publication2018
AuthorsMeryem, Amar, Samira, Douzi, Bouabid, El Ouahidi
Conference NameProceedings of the 2018 International Conference on Software Engineering and Information Management
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5438-7
KeywordsCloud Security, Deviation metric, k-means, log files, MapReduce, Metrics, pubcrawl, security metrics
AbstractMany 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.
URLhttp://doi.acm.org/10.1145/3178461.3178462
DOI10.1145/3178461.3178462
Citation Keymeryem_enhancing_2018