Journal
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
Volume 23, Issue 4, Pages 3221-3231Publisher
SPRINGER
DOI: 10.1007/s10586-020-03082-6
Keywords
Intrusion detection system; Cloud computing; DDoS attacks; Machine learning classifiers; Cyber security; FCM; SVM
Funding
- Faculty of Computer Systems and Software Engineering (FSKKP), Univer-siti Malaysia Pahang (UMP), Malaysia
- ST Engineering Electronics-SUTD Cyber Security Laboratory, Singapore University of Technology and Design (SUTD), Singapore
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Cloud computing offer various services over the Internet based on pay-per-use concept. Therefore, many organizations have already adopted this system to attract the users with its desirable features. However, due to its design, makes it vulnerable to malicious attacks. This demands an Intrusion Detection System that can detect such attacks with high detection accuracy in cloud environment. This paper proposes a novel intrusion detection system that combines a fuzzy c means clustering (FCM) algorithm with support vector machine (SVM) to improve the accuracy of the detection system in cloud computing environment. The proposed system is implemented and compared with existing mechanisms. The NSL-KDD dataset is used for experiments. Based on performance evaluation and comparative analysis, the results obtained using this new hybrid mechanism (FCM-SVM) show that the proposed system can detect the anomalies with high detection accuracy and low false alarm rates over the existing techniques.
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