期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2011
卷号：1
期号：3-1
出版社：Seventh Sense Research Group
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 802.11specific 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
