Misbehavior detection framework for community-based cloud computing

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dc.contributor.author Abdel Wahab, Omar
dc.contributor.author Bentahar, Jamal
dc.contributor.author Otrok, Hadi
dc.contributor.author Mourad, Azzam
dc.date.accessioned 2017-03-09T13:50:24Z
dc.date.available 2017-03-09T13:50:24Z
dc.date.issued 2017-03-09
dc.identifier.isbn 9781467381031 en_US
dc.identifier.uri http://hdl.handle.net/10725/5347
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.title Misbehavior detection framework for community-based cloud computing en_US
dc.type Conference Paper / Proceeding en_US
dc.author.school SAS en_US
dc.author.idnumber 200904853 en_US
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.keywords Computer architecture en_US
dc.keywords Monitoring en_US
dc.keywords Support vector machines en_US
dc.keywords Cloud computing en_US
dc.keywords Electronic mail en_US
dc.keywords Training en_US
dc.identifier.doi http://dx.doi.org/10.1109/FiCloud.2015.94 en_US
dc.identifier.ctation Wahab, O. A., Bentahar, J., Otrok, H., & Mourad, A. (2015, August). Misbehavior detection framework for community-based cloud computing. In 2015 3rd International Conference on Future Internet of Things and Cloud (pp. 181-188). IEEE. en_US
dc.conference.date 24-26 Aug., 2015 en_US
dc.conference.pages 181-188 en_US
dc.conference.place Rome, Italy en_US
dc.conference.title 2015 3rd International Conference on Future Internet of Things and Cloud en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url http://ieeexplore.ieee.org/abstract/document/7300816/ en_US
dc.orcid.id https://orcid.org/0000-0001-9434-5322
dc.publication.date 2015 en_US
dc.author.affiliation Lebanese American University en_US

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