An Effective Way of Cloud Intrusion Detection System using Decision tree, Support Vector Machine and Naïve Bayes Algorithm
T. Nathiya1, G. Suseendran2
1T. Nathiya, Ph.D. Research Scholar, Department of Computer Science, School of Computing Science, Vels Institute of Science, Technology & Advanced Studies VISTAS, Chennai (Tamil Nadu), India.
2G. Suseendran, Department of Information Technology, School of Computing Science, Vels Institute of Science, Technology & Advanced Studies VISTAS, Chennai (Tamil Nadu), India.
Manuscript received on 13 December 2018 | Revised Manuscript received on 25 December 2018 | Manuscript Published on 24 January 2019 | PP: 38-43 | Volume-7 Issue-4S2 December 2018 | Retrieval Number: Es2036017519/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cloud computing is a vast area, use the resources with cost-effectively. The service provider is to share the resources anywhere at any time. But the network is the most vital to accessing data in the cloud. The cloud malicious takes advantages while using the cloud network. Intrusion Detection System (IDS) is monitoring the network and notifies attacks. In Intrusion Detection System, anomaly technique is most important. Whenever Virtual Machine is created, IDS track the known and unknown data’s. If any unknown data found, Intrusion Detection System detects the data using anomaly classification algorithm and send the report to admin. This paper proposes we are using support vector machine (SVM), Naive Bayes, and decision tree (J48) algorithms for predicting unwanted data’s. In these algorithms are help us to overcome the high false alarm rate. Our proposed work implemented part using the WEKA tool to give a statistical report, which gives a better outcome in little calculation time.
Keywords: SVM, Naive Bayes, Decision Tree (J48), NSL-KDD Dataset, H-IDS.
Scope of the Article: Algorithm Engineering