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Intrusion detection system using PCA and Fuzzy PCA techniques

One of the most commonly problem in the field of network intrusion detection system is the tremendous number of redundant and irrelevant information used to build an intrusion detection system. In order to overcome this problem, we have used and compared two dimensionality reduction methods namely PCA and Fuzzy PCA which allows us to keeping just the most relevant information from the network traffic data. Then, we have applied K nearest Neighbour algorithm in order to classify the test samples of connections into a normal or attack category. The conducted experiments were made by using KDDcup99 dataset. The results obtained reveal that Fuzzy PCA method outperforms PCA in detecting U2R and DoS (Denial of Service) attacks.