关键词: 入侵检测, 果蝇优化算法, 核主成分分析, 加权极限学习机

Abstract:

The intrusion detection system based on machine learning generally has the problem that the classification performance is seriously affected by the large dimension of the intrusion data, the unbalanced data sample and the large dispersion degree. This paper proposes an intrusion detection algorithm based on LFKPCA-DWELM. First, the improved fruit fly algorithm（LFOA） is used to optimize the kernel principal component algorithm（KPCA）, and then the optimized kernel principal component algorithm（LFKPCA） is used to extract the features of the data. After that, it uses the processed data for training based on data dispersion beyond the extreme learning machine（DWELM）, and finally uses the trained model for classification experiments. Experimental results show that the algorithm can effectively improve the detection rate and reduce the false alarm rate and detection time.

Key words: intrusion detection, fly optimization algorithm, kernel principal component analysis, weighted extreme learning machine