期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：5
页码：1887-1893
出版社：TechScience Publications
摘要：Intrusion detection is a process of identifying and responding to malicious activity targeted at computing and networking resources. A wireless IDS is unique in that it detects attacks against the 802.11 frame at layer two of the wireless network. There are three different types of 802.11 MAC frames; data, control, and management. The Objective of this project is to design a self learning system that will reduce the numbers of features and that will accurately determine the attacks at the Data Link Layer. Our model for feature selection uses the information gain ratio measure as a means to compute the relevance of each feature and the k-means classifier to select the optimal set of MAC layer features that can improve the accuracy of intrusion detection systems while reducing the learning time of their learning algorithm. The optimization of the feature set for wireless intrusion detection systems on the performance and learning time of different types of classifiers based on neural networks. Experimental results with three types of neural network architectures clearly show that the optimization of a wireless feature set has a significant impact on the Efficiency and accuracy of the intrusion detection system.
