Enhanced Detection Rate for Network Intrusion Detection System by Using Chaotic Firefly Algorithm
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2018, Vol 20, Issue 6
Abstract
Regarding the security of computer systems, the intrusion detection systems (IDS) are essential components for the detection of attacks at the early stage. Therefore, the main goal of this thesis is to choose the differentiating features the development of an optimal machine learning algorithm with respect to high detection rates, fast training and testing processes. So a proposed machine learning model containing a feature selection algorithm (wrapper type) based on the integration of Firefly algorithm (FA) with Naïve Bayesian Classifier (NBC) were proposed. 1999 KDDCUP and NSL- KDD data sets . The proposed model been developed and tested over two types of feature selection objectives single objective fitness function (accuracy) and multiple objective fitness function (accuracy and number of features).
Authors and Affiliations
Dr. Sefer Kurnaz, Rafid Faeq Ahmed
Keywords
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- EP ID EP415620
- DOI 10.9790/0661-2006020107.
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