Abstract
Multitenant structure of PaaS cloud delivery model allows customers to share the platform resources in the cloud. However, this structure requires a strong security mechanism that isolates customer applications to prevent interference between different applications. In this paper, a malicious thread behavior detection framework using machine learning algorithms is proposed to classify whether user requests are malicious. The framework uses thread metrics of worker threads and N-Gram frequencies of operations as its features. Test results are evaluated on a real-life scenario using Random Forest, Adaboost and Bagging ensemble learning algorithms and evaluated using different accuracy metrics. It is found that the malicious request detection accuracy of the proposed system is 87.6%.
Original language | English |
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Title of host publication | CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science |
Editors | Victor Mendez Munoz, Donald Ferguson, Markus Helfert, Claus Pahl |
Publisher | SciTePress |
Pages | 418-425 |
Number of pages | 8 |
ISBN (Electronic) | 9789897582950 |
DOIs | |
Publication status | Published - 2018 |
Event | 8th International Conference on Cloud Computing and Services Science, CLOSER 2018 - Funchal, Madeira, Portugal Duration: 19 Mar 2018 → 21 Mar 2018 |
Publication series
Name | CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science |
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Volume | 2018-January |
Conference
Conference | 8th International Conference on Cloud Computing and Services Science, CLOSER 2018 |
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Country/Territory | Portugal |
City | Funchal, Madeira |
Period | 19/03/18 → 21/03/18 |
Bibliographical note
Publisher Copyright:Copyright © 2018 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
Keywords
- Cloud security
- Machine learning
- Malicious behavior
- PaaS