期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：6
DOI：10.15680/ijircce.2015.0306058
出版社：S&S Publications
摘要：Cyber security threats have become increasingly sophisticated and complex. Intrusion detection which isone of the major problems in computer security has the main goal to detect infrequent access or attacks and toprotect internal networks. A new hybrid intrusion detection method combining multiple classifiers for classifyinganomalous and normal activities in the computer network is presented. The misuse detection model is builtbased on the C5.0 Decision tree algorithm and using the information collected anomaly detection model is builtwhich is implemented by one class Support Vector Machine (SVM).The key idea is to take advantage of cuttlefishalgorithm (CFA).In the proposed algorithm, Cuttlefish can find best selected features to remove the redundant andirrelevant features to evaluate the accuracy of classification. Integration of multiple algorithms helps to get betterperformance. The Experimental results are performed on NSL-KDD Dataset, and it is shown that overall performanceof the proposed approach is improved in terms of detection rate and low false alarms rate in comparison to the existingtechniques.
