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...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , |
| التنسيق: | conferenceObject |
| منشور في: |
2017
|
| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/5347 http://dx.doi.org/10.1109/FiCloud.2015.94 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php http://ieeexplore.ieee.org/abstract/document/7300816/ |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513465817759744 |
|---|---|
| author | Abdel Wahab, Omar |
| author2 | Bentahar, Jamal Otrok, Hadi Mourad, Azzam |
| author2_role | author author author |
| author_facet | Abdel Wahab, Omar Bentahar, Jamal Otrok, Hadi Mourad, Azzam |
| author_role | author |
| dc.creator.none.fl_str_mv | Abdel Wahab, Omar Bentahar, Jamal Otrok, Hadi Mourad, Azzam |
| dc.date.none.fl_str_mv | 2017-03-09T13:50:24Z 2017-03-09T13:50:24Z 2017-03-09 |
| dc.identifier.none.fl_str_mv | 9781467381031 http://hdl.handle.net/10725/5347 http://dx.doi.org/10.1109/FiCloud.2015.94 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. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php http://ieeexplore.ieee.org/abstract/document/7300816/ |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Misbehavior detection framework for community-based cloud computing |
| dc.type.none.fl_str_mv | Conference Paper / Proceeding info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | LAURepo_cbf6b25aaa3c542cd7b4b93e0181618b |
| identifier_str_mv | 9781467381031 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. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/5347 |
| publishDate | 2017 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Misbehavior detection framework for community-based cloud computingAbdel Wahab, OmarBentahar, JamalOtrok, HadiMourad, AzzamThe 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.N/AIEEE2017-03-09T13:50:24Z2017-03-09T13:50:24Z2017-03-09Conference Paper / Proceedinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject9781467381031http://hdl.handle.net/10725/5347http://dx.doi.org/10.1109/FiCloud.2015.94Wahab, 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.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttp://ieeexplore.ieee.org/abstract/document/7300816/eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/53472021-03-19T10:03:19Z |
| spellingShingle | Misbehavior detection framework for community-based cloud computing Abdel Wahab, Omar |
| status_str | publishedVersion |
| title | Misbehavior detection framework for community-based cloud computing |
| title_full | Misbehavior detection framework for community-based cloud computing |
| title_fullStr | Misbehavior detection framework for community-based cloud computing |
| title_full_unstemmed | Misbehavior detection framework for community-based cloud computing |
| title_short | Misbehavior detection framework for community-based cloud computing |
| title_sort | Misbehavior detection framework for community-based cloud computing |
| url | http://hdl.handle.net/10725/5347 http://dx.doi.org/10.1109/FiCloud.2015.94 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php http://ieeexplore.ieee.org/abstract/document/7300816/ |