Abstract
Feature selection algorithm plays a crucial role in intrusion detection, data mining and pattern recognition. According to some evaluation criteria, it gets optimal feature subset by deleting unrelated and redundant features of the original data set. Aiming at solving the problems about the low accuracy, the high false positive rate and the long detection time of the existing feature selection algorithm. In this paper, we come up with a feature selection algorithm towards efficient intrusion detection, this algorithm combines the correlation algorithm and redundancy algorithm to chooses the optimal feature subset. Experimental results show that the algorithm shows almost and even better than the traditional feature selection algorithm on the different classifiers.
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