An Improved HNIDS in Cloud Real Time Prediction Using Fuzzy Decision Making Combination Rule
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 23 April 2019 | Revised Manuscript received on 02 May 2019 | Manuscript Published on 08 May 2019 | PP: 261-267 | Volume-7 Issue-5S3 February 2019 | Retrieval Number: E11490275S19/19©BEIESP
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© 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: Fuzzy Decision Making (FDM) combination rule can be used to improve the real time prediction in cloud. Needs the dynamic approach in day to day to monitor the traffic and notify the illegal problems into system administrator. This type of approach is known as HNIDS (Hybrid Network Intrusion Detection System). So our model HNIDS is introduce the FDM rule in this paper. Not only FDM we are using new upcoming classification learner XGBoost and SVM. SVM is functional dependent and XGBoost is decision tree type of classification. So that the two different type of classification model to predict the cloud network packets whether the packets are normal or abnormal. Finally FDM combination rule to take the decision using belief probability evidences. This is new type of prediction method. Result and Discussion have shown that Our HNIDS using the method to predict the network packets with high accuracy value and minimum computation cost efficiency.
Keywords: HNIDS, SVM, XGBoost, Fuzzy Decision Making Rule, NSL- KDD Datasets, Python, Azure Cloud.
Scope of the Article: Fuzzy Logics