Abstract
Within the last years, IT security has become a major economic issue, Intrusion detection systems are an effective way to detect malicious connections in a computer Network. The network traffic data employed to build an intrusion detection system (IDS) is always large with ineffective information, that what decrease it efficiency.to address this problem, we must to keep just the worthless information from the original high dimensional data by using a feature extraction technique. One of the most used in this field is Linear Discriminant Analysis unfortunately this method uses between-class scatter against within-class scatter which are almost always singular. In this paper, we suggest to use the optimal mean PCA as prestep before LDA. Many experiments on KDDcup99 and NSL-KDD indicate the superiority of the proposed technique.
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