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Performance Analysis of Intrusion Detection Systems Implemented using Hybrid Machine Learning Techniques

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Purushottam R. Patil, Yogesh Sharma, Manali Kshirasagar

Purushottam R Patil, Yogesh Sharma and Manali Kshirasagar. Article: Performance Analysis of Intrusion Detection Systems Implemented using Hybrid Machine Learning Techniques. International Journal of Computer Applications 133(8):35-38, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Purushottam R. Patil and Yogesh Sharma and Manali Kshirasagar},
	title = {Article: Performance Analysis of Intrusion Detection Systems Implemented using Hybrid Machine Learning Techniques},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {8},
	pages = {35-38},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Intrusion Detection System (IDS) are said to be more effective when it has both high intrusion detection (true positive) rate and low false alarm (false positive). But current IDS when implemented using data mining approach like clustering, classification alone are unable to give 100 % detection rate hence lack effectiveness. In order to overcome these difficulties of the existing systems, many researchers implemented intrusion detection systems by integrating clustering and classification approach like k-means and Fuzzy logic, K-means and genetic algorithm, some of the researcher also tried use of Decision tree and Neural Network to detect unknown attacks. In this paper analysis of such Hybrid systems which are implemented by using the benchmark dataset compiled for the 1999 KDD intrusion detection contest, by MIT Lincoln Labs.


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Intrusion detection system (IDS), Detection rate in IDS, False alarm Rate, Classification, Prediction, MIT KDD’99 dataset.