稀疏表示在入侵检测中的应用

摘要

This paper proposes an intrusion detection algorithm based on sparse representation. The sparsity constraints are imposed to over-complete dictionary learning and sparse coding so that the sparse coefficients have better reconstruction and discrimination. The discriminative K-SVD algorithm is exploited to optimize the dictionary and the linear discriminative function, and then extracted features are fed into a linear classifier to implement the intrusion detection. Experimental results show that the algorithm achieves lower false alarm rate and higher detection rate, and it has a good performance in intrusion detection.%提出一种基于稀疏表示的入侵检测算法.将稀疏性约束引入过完备词典学习和编码过程中,使学习得到的稀疏系数可以保持较好的重构性,同时增强判别力.利用判别式K-SVD算法优化过完备词典和线性判别函数,将提取的稀疏特征作为线性分类器的输入,实现入侵检测.实验结果表明,该算法可以获得较低的误报率和较高的检测率,分类性能较好.