A Real-Time Anomaly Network Intrusion Detection System with High Accuracy
in-depth of computer networks. However, building an efﬁcient IDS faces a number of challenges. One of the important
challenges is dealing with data containing high number of features. This paper is devoted to solve this challenge by proposing
an effective PSO-Discritize-HNB intrusion detection system. The proposed PSO-Discritize-HNB IDS combines Particle Swarm
Optimization (PSO) and Information Entropy Minimization (IEM) discritize method with the Hidden Na¨ ıve Bayes (HNB) classiﬁer.
To evaluate the performance of the proposed network IDS several experiments are conducted on the NSL-KDD network intrusion
detection dataset. A comparative study of applying Information Gain (IG) which is a well known feature selection algorithm with
HNB classiﬁer was accomplished. Also, to validate the proposed PSO-Discritize-HNB network intrusion detection; it is compared with
different feature selection methods as Principal Component Analysis (PCA) and Gain Ratio. The results obtained showed the adequacy
of the proposed network IDS by reducing the number of features from 41 to 11, which leads to high intrusion detection accuracy
(98.2%) and improving the speed to 0.18 sec.
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