A Review on Hybrid Intrusion Detection System Using TAN & SVM

  • Potteti S
  • Parati N
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-The dramatically development of internet, Security of network traffic is becoming a major issue of computer network system. Attacks on the network are increasing day-by-day. The Hybrid framework would henceforth, will lead to effective, adaptive and intelligent intrusion detection. In this paper, We propose a hybrid fuzzy rough with Naive bayes classifier, Support Vector Machine and K-nearest neighbor (K-NN) based classifier (FRNN) to classify the patterns in the reduced datasets, obtained from the fuzzy rough bioinspired algorithm search. The proposed hybrid is subsequently validated using real-life datasets obtained from the University of California, Irvine machine learning repository. Simulation results demonstrate that the proposed hybrid produces good classification accuracy. Finally, parametric and nonparametric statistical tests of significance are carried out to observe consistency of the classifiers.




Potteti, S., & Parati, N. (2015). A Review on Hybrid Intrusion Detection System Using TAN & SVM. IJCSN International Journal of Computer Science and Network ISSN, 4(3), 2277–5420. Retrieved from www.IJCSN.org

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