关键词: 入侵检测, 粗糙集理论, 支持向量机

Abstract: This thesis proposes the intrusion detection algorithm based on rough set and the improved least squares support vector machine. The algorithm reduces sample attributes by discernible matrix using rough set theory, reduces the dimension of the data samples. It improves the least squares support vector machine by a sparse algorithm, so it can improve the veracity of data sample classification with the sparse characteristic and rapid detection. On the one hand the combined algorithm has the advantages that rough set can reduce the data effectively and the support vector machine can classify accurately, and on the other hand it avoids the poor generalization while the rough set is in the noise environment and overcomes the limitations when support vector machine identifies effective data and redundant data. Experimental results show that intrusion detection algorithm based on rough set and the improved least squares support vector machine has high detection accuracy, low false positive rate and false negative rate and short detection time which show the validity of the algorithm.

Key words: intrusion detection, rough sets theory, support vector machine