【摘要】 为了在攻击形式多样化、入侵数据海量及多维化的环境中快速、准确地识别网络攻击,提出了一种融合Fisher-PCA特征提取与深度学习的入侵检测算法。通过Fisher特征选择算法选出重要的特征组成特征子集,然后基于主成分分析法(Principal component analysis,PCA)将特征子集进行降维,提取出了分类能力强的特征集。构建了一种新的深度神经网络(Deep neural networks,DNN)模型对网络攻击数据和正常数据进行识别与分类。在KDD99数据集上进行实验,结果表明:与传统的人工神经网络(Artificial neural network,ANN)和支持向量机(Support vector machine,SVM)算法相比,这种入侵检测算法的准确率分别提高了12.63%和6.77%,误报率由原来的2.31%和1.96%降为0.28%;与DBN4和PCA-CNN算法相比,在准确率和检测率保持基本相同的同时有着更低的误报率。更多还原

【Abstract】 To quickly and accurately identify network attacks in a multi-dimensional environment with diversified attack forms and massive intrusion data,an intrusion detection model combining Fisher-PCA feature extraction and deep learning is proposed. Firstly,the Fisher feature selection algorithm selects important features to form feature subsets. Then the dimension of the feature subsets is reduced based on principal component analysis(PCA)and the feature set with strong classification ability is extracted. A new deep neural network(DNN)is constructed to identify and classify network attack data and normal data. Experimental results on KDD99 dataset show that compared with the traditional artificial neural network(ANN)and support vector machine(SVM)algorithms,the accuracy of this intrusion detection algorithm can be improved by 12.63% and 6.77%,respectively,and the false alarm rate is reduced from2.31% and 1.96% to 0.28%. Compared with DBN4 and PCA-CNN algorithms,its accuracy and detection rate are basically the same,while the false alarm rate is lower.更多还原