非负矩阵分解在免疫入侵检测中的优化和应用

Optimization and Application of Non-negative Matrix Factorization in Immune Intrusion Detection

针对免疫入侵检测数据处理速度慢以及检测实时性差的问题,提出Bregman非负矩阵分解算法,采用Bregman迭代方式改进传统非负矩阵分解过程,优化矩阵迭代过程,利用矩阵本地化方法分解矩阵,增加矩阵的约束,保留检测数据内部结构并且加快数据的处理速度。在KDD CUP 1999数据集上的仿真结果表明,该算法有效提高了入侵检测速度,增强了免疫入侵检测的时效性。

To deal with the problem of slow data processing speed and poor timeliness of immune intrusion detection, non-negative matrix factorization by Bregman iteration is proposed. It improves the traditional method, changes matrix iteration process, and uses matrix location to realize the decomposition conditions and its constraint, better retention of the internal structure of the data and acceleration of the processing. Experimental results in KDD CUP 1999 datasets show that the approach can improve the speed of intrusion detection and enhance the timeliness of immune intrusion detection.