基于主成分分析和循环神经网络的入侵检测模型

针对网络数据特征维度高、现有的入侵检测方法准确率低的问题,该文提出了一种基于主成分分析(PCA)和循环神经网络(RNN)的入侵检测方法PCA-RNN。该方法先对网络数据进行预处理,通过主成分分析法对数据进行特征降维和降噪,找出含有最大信息的主成分特征子集,然后对处理后的数据使用循环神经网络进行分类训练。实验使用基于Python的TensorFlow平台,并采用NSL-KDD作为实验数据集。实验结果表明,与常用的基于机器学习和深度学习方法的入侵检测技术相比较,该文提出的入侵检测方法可有效地提高检测的准确性。

Intrusion Detection Model Based on Principle Component Analysis and Recurrent Neural Network

To address the issue of high feature dimension of network data for a better intrusion detection method, this paper proposes an intrusion detection method based on PCA(principal component analysis) and RNN(recurrent neural network). PCA is used to perform feature dimension reduction and noise reduction on the data, detecting the subset of principal component features with the largest information. And then RNN is used to classify the processed data. Experimented on the NSL-KDD data set, the results show that the proposed intrusion detection algorithm can effectively improve the accuracy of detection compared with the popular intrusion detection technology based on machine learning and deep learning methods.