Intrusion detection is the process of monitoring and analyzing the events occurring in a computer system in order to detect signs of security problems. Today most of the intrusion detection approaches focused on the issues of feature selection or reduction, since some of the features are irrelevant and redundant which results lengthy detection process and degrades the performance of an intrusion detection system (IDS). The objective of this paper is to construct a lightweight Intrusion Detection System (IDS) aimed at detecting anomalies in networks. The crucial part of building lightweight IDS depends on preprocessing of network data, identifying important features and in the design of efficient learning algorithm that classify normal and anomalous patterns. The design of IDS is investigated from these three perspectives. The goals are to remove redundant instances that causes the learning algorithm to be unbiased, identify suitable subset of features by employing a wrapper based feature selection algorithm and realizing proposed IDS with neurotree to achieve better detection accuracy.

1. Siva S. Sivatha Sindhu , S. Geetha , A. Kannan" Decision tree based light weight intrusion detection using a wrapper approach", Expert Systems with Applications 39 (2012) 129–141.
2. Kapil Kumar Gupta, Baikunth Nath, Ramamohanarao Kotagiri," Layered Approach Using Conditional Random Fields for Intrusion Detection" IEEE Transactions On Dependable And Secure Computing, Vol. 7, No. 1, January- March 2010.
3. Mehdi MORADI and Mohammad ZULKERNINE," A Neural Network Based System for Intrusion Detection and Classification of Attacks", Natural Sciences and Engineering Research Council of Canada (NSERC).
4. Bertrand Portier ,Froment-Curtil," Data Mining Techniques for Intrusion Detection", The University of Texas at Austin, Spring 2000 Dr. Ghosh – EE380L Data Mining Term Paper.
5. L Prema RAJESWARI, Kannan ARPUTHARAJ," An Active Rule Approach for Network Intrusion Detection with Enhanced C4. 5 Algorithm", I. J. Communications, Network and System Sciences, 2008, 4, 284-359Published Online November 2008 in SciRes . (http://www. SRPublishing. org/ journal/ijcns/)
6. Ahmed H. Fares* and Mohamed I. Sharawy ," Intrusion Detection: Supervised Machine Learning", Journal of Computing Science and Engineering, Vol. 5, No. 4, December 2011.
7. Nahla Ben Amor, Salem Benferhat," Naive Bayes vs Decision Trees in Intrusion Detection Systems" , SAC'04, March 14-17, 2004, Nicosia, Cyprus.
8. Dr. Saurabh Mukherjeea, Neelam Sharma," Intrusion Detection using Naive Bayes Classifier with Feature Reduction", Procedia Technology 4 ( 2012 ) 119 – 128.
9. Tom V. Mathew," Genetic Algorithm", Indian Institute of Technology Bombay, Mumbai-400076.
10. Simon Haykin,"Feed Forward Neural networks:an introduction"
11. Richard Power. 1999 CSI/FBI computer crime and security survey. Computer Security Journal,Volume XV (2), 1999
12. SANS Institute—Intrusion Detection FAQ, http://www. sans. org/resources/idfaq/, 2010.
13. Overview of Attack Trends, http://www. cert. org/archive/pdf/attack_trends. pdf, 2002.
14. K. K. Gupta, B. Nath, R. Kotagiri, and A. Kazi, "Attacking Confidentiality: An Agent Based Approach," Proc. IEEE Int'l Conf. Intelligence and Security Informatics (ISI'06), vol. 3975, pp. 285-296, 2006.

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