Enhancing the Capability of IDS using Fuzzy Rough Classifier with Genetic Search Feature Reduction


  • Ashalata Panigrah Department of Computer Science & Engineering, SMIT, Berhampur
  • Manas Ranjan Patra Department of Computer Science, Berhampur University, Berhampur




Rapid expansion of computer network throughout the world has made security a crucial issue in a computing environment. In the recent past several cyber attacks have corrupted data of many organizations and creating serious problems. Intrusion Detection System which are increasingly a key part of system defense are used to identify abnormal activities in a computer system. The success of an intrusion detection system depends on the selection of the appropriate features in detecting the intrusion activity.   Experiments have been conducted using four classifier techniques , viz, Fuzzy NN, Fuzzy Rough NN, VQNN, Fuzzy Rough Ownership NN. We have studied the accuracy, recall, precision, false alarm  rate, error rate of all the classifier techniques


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How to Cite

Panigrah, A., & Ranjan Patra, M. (2014). Enhancing the Capability of IDS using Fuzzy Rough Classifier with Genetic Search Feature Reduction. Discoveries in Agriculture and Food Sciences, 2(2), 01–13. https://doi.org/10.14738/tnc.22.97