文章基本信息

标题：An Efficient Hybrid Classifier Model for Anomaly Intrusion Detection System
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作者：Asghar Ali Shah ; M. Khurram Ehsan ; Kashif Ishaq 等
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2018
卷号：18
期号：11
页码：127-135
出版社：International Journal of Computer Science and Network Security
摘要：Ensuring security has always been a challenging problem for both customized network solutions and information systems. Intrusion Detection System (IDS) is playing a very important role to ensure security both in network solutions and information systems. Significant efforts has already been made and many efforts are underway to improve the IDS but still there are many short comings. This study proposed a model based on extensive survey to create an efficient hybrid classifier which is jointly based on feature selection, parameter optimization and classification. Feature selection is adapted to refine the area of interest by improving the accuracy of classification, then to optimize the parameters, genetic algorithm (GA) is the most appropriate technique to be used. Parameters optimization using GA also plays a remarkable role to improve classification using support vector machine (SVM). SVM is considered a suitable machine learning technique for classification of intrusions which are detected both in networks and information systems. Finally SVM will classify the observed activity as normal or attack using adopted linear or nonlinear techniques. The proposed solution paves a way to improve accuracy by efficiently detecting the intrusions within real time applications of network and systems.
关键词：IDS; Classification; SVM; GA; Attacks types