DOIONLINE 
DOIONLINE Publish In |
International Journal of Soft Computing And Artificial Intelligence (IJSCAI)-IJSCAI |
 Journal Home Volume Issue |
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Issue |
Volume-3,Issue-2 ( Nov, 2015 ) | |||||||||
Paper Title |
A Hybrid Intrusion Detection System Using Particle Swarm Optimization For Feature Selection | |||||||||
Author Name |
Sedigheh Khajouei Nejad, Sam Jabbehdari, Mohammad Hossein Moattar | |||||||||
Affilition |
Computer Engineering Department, Sirjan Branch, Islamic Azad University, Sirjan, Iran Computer Engineering Department, North Tehran Branch, Islamic Azad University, Tehran, Iran Computer Engineering Department, Mashhad Branch, Islamic Azad University, Mashhad, Iran | |||||||||
Pages |
55-58 | |||||||||
Abstract |
The ultimate goal of this paper is to develop systems for intrusion detection in computer networks to achieve the best accuracy performance. This study suggests the idea of classifier combination. This hybrid approach is based on optimization and feature selection using a combination of two-step approach for the classification. In the first step, using Accelerated Particle Swarm Optimization (APSO) algorithm, a set of best discriminating features is selected. Then using a combination of three classifiers namely KNN, Decision Tree and Neural Network, intermediate data is generated. Finally these data is fed to the AdaboostM2 classifier for final decision. The performance of the proposed approach is evaluated with criteria such as F measure, Accuracy and False Alarm. Experiments on KDD-CUP99 dataset show the effectiveness of the proposed approach compared to the most recent approaches in this context. Index Terms- Intrusion Detection, Hybrid Classifier, Particle Swarm Optimization, Adaboost, Feature selection | |||||||||
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