Using Naïve Bayes Classifier to Accelerate Constructing Fuzzy Intrusion Detection Systems
Mehran Amiri1, Mahdi Eftekhari2, Farshid Keynia3
1Mehran Amiri, Computer engineering department of Science and Research branch of Islamic Azad University, Kerman, Iran.
2Mahdi Eftekhari, Computer engineering department of Science and Research branch of Islamic Azad University, Kerman, Iran.
3Farshid Keynia, Computer engineering department of Science and Research branch of Islamic Azad University, Kerman, Iran.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 453-459 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1190112612/2013©BEIESP
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© The Authors. Published By: 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: A Bayesian classifier is one of the most widely used classifiers which possess several properties that make it surprisingly useful and accurate. It is illustrated that performance of Bayesian learning in some cases is comparable with neural networks and decision trees. Bayesian theorem suggests a straight forward process which is not based on search methods. This is the major point which satisfies the marvelous time complexity of Bayesian classifier. At the other hand, constructing phase of fuzzy intrusion detection systems suffer from time consuming processes which are based on search methods. In this paper we propose a novel method to accelerate such processes using Bayesian inference. Experimental results show meaningful time reduction.
Keywords: Fuzzy intrusion detection systems, Naïve Bayes classifier, Rule`s consequent class, Time complexity.