ANALYZING THE MACHINE LEARNING ALGORITHMS- NAÏVE BAYES, RANDOM TREE, AND SUPPORT VECTOR MACHINES SVM USING THE KDD99 DATA SET TO PREDICT AND CLASSIFY THE INTRUSION DETECTION SYSTEM USING WEKA

Saumya Gupta

Vol. 2, Jan-Dec 2016

Page Number: 452-459

Abstract:

Objectives/Methods: The growing prevalence of network attacks is an issue that can affect the availability, confidentiality, and integrity of critical information for companies. Thus, Intrusion detection systems are increasingly being used to identify special access or attacks to secure internal networks. In this study, we will outline the evolution of extensive data in the intrusion detection system, and apply three supervised learning methods, namely: Naïve Bayes, Random Tree, and Support Vector Machines SVM, using the kdd99 data set. The purpose of this research is to detect and predict attacks in order to take preventive action against intrusion risks. Findings: Investigational results have demonstrated that the random tree gives the highest accuracy at 100%. The results will be useful in choosing the best classification machine learning algorithm for intrusion prediction. Application/Improvements: for simulation and testing the performance of algorithms, we have used WEKA (Waikato environment for knowledge analysis), which includes tools for data preparation, classification, regression, clustering, association rule extraction, and visualization

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