中文摘要：

针对经典BP神经网络在入侵检测应用中收敛速度慢、学习性能不够理想等缺陷,以消除原始数据中的冗余信息、提升入侵检测算法的检测性能为目的,综合采用主成分分析法和附加动量法,提出了一种基于PCA-BP神经网络的入侵检测方法,通过对数据的特征选择和对网络的权值修正,对经典BP神经网络算法进行了拓展和改进。首先对网络数据集进行标准化处理,并对处理后的数据集进行降维处理以确定主分量的特征数,最后将处理完成后的数据集输入到改进的BP神经网络中进行检测。通过在KDD Cup 1999网络数据集上的大量实验证明,该方法在大部分网络环境,尤其是在训练样本较为充足的网络环境中时,系统模型的收敛性、检测效率和检测准确率上均优于经典BP神经网络方法和半监督入侵检测方法。

英文摘要：

Aimed at the problems that slow convergence speed, poor learning performance and other imper- fections exist in the classical BP neural network intrusion detection, a PCA-BP neural network intrusion detection method is put forward by adopting principal components analysis and additional momentum method, This method improves the classical BP neural network algorithm by data features selection and network weights amendment. Firstly, the paper standardizes the network data set, and then adopts it to deal with dimension reduction to confirm the characteristics. Finally, the paper detects the processed data set by improved BP neural network. Through the lots of experiments in KDD Cup 1999 network data sets, the result shows that the method has better performances in system model convergence, detection efficien- cy and detection accuracy in most network environment. Especially, in training samples, the convergence of system model, the detection efficiency and the detection accuracy are better than that by using BP neural network algorithm and half-supervision intrusion detection algorithm.