期刊名称：International Journal of Computer Science and Communication Networks
电子版ISSN：2249-5789
出版年度：2011
卷号：1
期号：2
页码：165-170
出版社：Technopark Publications
摘要：Intrusion Detection Systems (IDSs) are a major line of defense for protecting network resources from illegal penetrations. A common approach in intrusion detection models, specifically in anomaly detection models, is to use classifiers as detectors. Selecting the best set of features is central to ensuring the performance, speed of learning, accuracy, and reliability of these detectors as well as to remove noise from the set of features used to construct the classifiers. In most current systems, the features used for training and testing the intrusion detection systems consist of basic information related to the TCP/IP header, with no considerable attention to the features associated with lower level protocol frames. The resulting detectors were efficient and accurate in detecting network attacks at the network and transport layers, but unfortunately, not capable of detecting 802.11-specific attacks such as deauthentication attacks or MAC layer DoS attacks
关键词：Feature selection; intrusion detection systems; K-means; information gain ratio; wireless networks; neural networks
