Malicious Intrusion Detection Using Machine Learning Schemes
Madhavi Dhingra1, S C Jain2, Rakesh Singh Jadon3
1Madhavi Dhingra Assistant Professor in Department of Computer Science and Engineering at Amity University Madhya Pradesh, Gwalior.
2Dr. S C Jain working as Director in Amity School of Engineering & Technology at Amity University Madhya Pradesh, Gwalior
3Dr. Rakesh Singh Jadon Professor & Head in Department of Computer Applications, Madhav Institute of Technology andScience,Gwalior (M.P.).
Manuscript received on July 30, 2019. | Revised Manuscript received on August 25, 2019. | Manuscript published on August 30, 2019. | PP: 4194-4198 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8839088619/2019©BEIESP | DOI: 10.35940/ijeat.F8839.088619
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Abstract: Wireless networks are continuously facing challenges in the field of Information Security. This leads to major researches in the area of Intrusion detection. The working of Intrusion detection is performed mainly by signature based detection and anomaly based detection. Anomaly based detection is based on the behavior of the network. One of the major challenge in this domain is to identify and detect the malicious node in wireless networks. The intrusion detection mechanism has to analyse the behavior of the node in the network by means of the several features possessed by each node. Intelligent schemes are the need of the hour in such scenario. This paper has taken a standard dataset for studying the features of the wireless node and reduced the features by applying the most efficient Correlation Attribute feature selection method. The machine learning algorithms are applied to obtain an effective training model which is then applied on the testing dataset to validate the model. The accuracy of the model is determined by the performance parameters such as true positive rate, false positive rate and ROC area. Neural network, bagging and decision tree algorithm RepTree are giving promising results in comparison with other classification algorithms. 
Keywords: Data Mining, Intrusion Detection, Classifier, Malicious