Ensemble of Classifiers for Intrusion Detection System
Sonali Kadam1, Rutuja Pawar2, Shweta Phule3, Priyansha Kher4, Manisha Kumari5
1Sonali Kadam, Bharati Vidhyapeeth’s College of Engineering for Women, Pune (Maharashtra). India.
2Rutuja Pawar, Bharati Vidhyapeeth’s College of Engineering for Women, Pune (Maharashtra). India.
3Shweta Phule, Bharati Vidhyapeeth’s College of Engineering for Women, Pune (Maharashtra). India.
4Priyansha Kher, Bharati Vidhyapeeth’s College of Engineering for Women, Pune (Maharashtra). India.
5Manisha Kumari, Bharati Vidhyapeeth’s College of Engineering for Women, Pune (Maharashtra). India.
Manuscript received on 15 February 2017 | Revised Manuscript received on 22 February 2017 | Manuscript Published on 28 February 2017 | PP: 205-209 | Volume-6 Issue-3, February 2017 | Retrieval Number: C4872026317/17©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The continuous growth in Network attacks is being a serious problem in software industry. Intrusion detection framework is utilized to distinguish and break down system assaults so IDS should be upgraded that can screen the framework and can trigger the readiness in the framework. Numerous calculations have been proposed by various creators to enhance the execution of IDS yet at the same time they can’t give appropriate or finish arrangement. In proposed framework creators perform probes distinctive blends of Bayesian system, Naïve Bayes, JRip, MLP, IBK, PART and J48 classifier. What’s more for each mix two pre-processing procedures Normalization and discretization will be connected. The advantage of proposed approach is the combination detecting majority attacks will be ensemble with the respective pre-processing technique. Hence, any kind attack in the network can be detected with best accuracy.
Keywords: Bayesian Network, Intrusion Detection System, IBK. JRip, J48, MLP, Naïve Bayes, PART.
Scope of the Article: Data Base Management System