基于复合分类器的网络入侵检测模型

Intrusion Detection Model Based on Hybrid Classifier

为提高国家电网网络入侵检测中攻击分类问题的准确度,提出一种基于复合分类器的入侵检测模型。复合分类器由核主成分分析、量子遗传算法和前馈(back propagation,BP)神经网络组合而成。复合分类器先使用核主成分分析将高维数的原始数据降维,降维后的数据再通过BP神经网络训练生成分类模型,其中BP神经网络的参数通过量子遗传算法优化得到,最后使用分类模型对待测样本做精确入侵检测分类。与传统入侵检测算法相比,基于复合分类器的入侵检测模型更准确。

In order to improve the accuracy of classification problem in intrusion detection for state grid,a novel intrusion detection model,which is based on the hybrid classifier,is proposed in this paper.The hybrid classifier is composed by kernel principal component analysis（KPCA）,back propagation neural network（BPNN） and quantum genetic algorithm（QGA）.In the hybrid classifier,KPCA is used to reduce dimensions of data.The classification model is trained by BPNN,of which the parameters are optimized by QGA.Based on the classification model,the data samples are classified by accurate intrusion detection.Compared with the traditional methods,the intrusion detection model based on hybrid classifier has better performance in reducing the calculation errors.