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Network Intrusion Detection Mistreatment Soft Computing Technique

P C Shende, D. Mude


With the approaching era of web, the network security has become the key foundation for tons of economic and business applications. Intrusion detection is one amongst the looms to resolve the matter of network security. An Intrusion Detection System (IDS) could be a program that analyses what happens or is going on throughout associate in nursing execution and tries to find indications that the pc has been abused. Here we propose a brand new approach by utilizing neuro fuzzy and support vector machine with fuzzy genetic algorithmic program for higher rate of detection.


Neuro fuzzy, support vector machine, fuzzy genetic algorithm, Intrusion Detection System

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David Wagner, Paolo Soto. Mimicry Attacks on Host Based Intrusion Detection Systems. Proc. of the 9th ACM Conf. on Computer and Communications Security. 2002; 255–264p.

Ghanshyam Prasad Dubey, Neetesh Gupta, Bhujade Rakesh K. A Novel Approach to Intrusion Detection System using Rough Set Theory and Incremental SVM. International Journal of Soft Computing and Engineering (IJSCE). 2011; 1(l): 1448p.

Hansung Lee, Jiyoung Song, Daihee Park. Intrusion Detection System Based on Multi-class SVM. Dept. of computer & Information Science, Korea Univ., Korea. 2005; 51U519p.

Jirapummin C, Wattanapongsakorn N, Kanthamanon P. Hybrid Neural Networks for Intrusion Detection System. Proceedings of ITC-CSCC. 2002; 928–931p.

Horeis T. Intrusion Detection with Neural Network-Combination of Self-Organizing Maps and Redial Basis Function Networks for Human Expert Integration. A Research Report. 2003. Available in hap:// EA C-Research-2003-Report-Horeis.pdf

Han SJ, Cho SB. Evolutionary Neural Networks for Anomaly Detection based on the Behavior of a Program. IEEE Trans. Syst. Man Cybern. B. 2005; 36(3): 559–570p.

Chen YH, Abraham A, Yang B. Hybrid Flexible Neural-Tree-Based Intrusion Detection Systems. International Journal of Intelligent Systems (IJIS). 2007; 22(4): 337–352p.

Chandrashekhar AM, Raguveer K. Performance Evaluation of Data Clustering Techniques using KDD Cup 99 Intrusion Data Set. International Journal of Information and Network Security (IJINS). 2012; 1(4): 294–305p.

Jang R. Neuro-Fuzzy Modeling: Architectures, Analysis and Applications. Ph D Thesis, University of California, Berkley. 1992.

Jose Vieira, Fernando Morgado Dias, Alexandre Mota. Neuro-Fuzzy Systems, a Survey. Proceedings International Conference on Neural Networks and Applications. 2004.

Buckley JJ, Hayashi Y, Czogala E. On the Equivalence of Neural Nets and Fuzzy Expert Systems, Fuzzy Sets & Systems. A Research Report. 1993; 129–134p.

Aickelin U, Twycross J, Hesketh-Roberts T. Rule Generalization in Intrusion Detection Systems using SNORT. International Journal of Electronic Security and Digital Forensics. 2007; 1(1): 101–116p.

Dietterich TG, Bakiri G. Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research (JAIR). 1995; 2: 263–286p.

Tavallaee M, Bagheri E, Lu W, et al. A Detailed Analysis of the KDD CUP 99 Data Set. Proceedings IEEE International Conference on Computational Intelligence for Security and Defense Applications, Ottawa, Ontario, Canada. 2009; 53–58p.


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