中圖分類號： TP393.08
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2021.07.007
引用格式： 陳解元. 基于LSTM的卷積神經(jīng)網(wǎng)絡異常流量檢測方法[J].信息技術(shù)與網(wǎng)絡安全,，2021，40(7)：42-46.

Network intrusion detection based on convolutional neural networks with LSTM

Abstract： As traditional machine learning methods rely on artificial feature extraction,there are problems such as low accuary and inability to deal with unknown types of attacks such as 0day vulnerability exploitation,，this paper proposed a hybrid algorithm based on Convolutional Neural Networks(CNN) and Long-Short Term Memory(LSTM) to fully explore the structural characteristics of attack traffic, extract the spatiotemporal characteristics of traffic data, and improve the performance of abnormal traffic detection system.The experimental results show that on the CIC-IDS2017 data set, the accuracy of various abnormal traffic detection is more than 96.9%, and the overall accuracy reaches 98.8%, which is higher than other machine learning algorithms, while maintaining a very low false alarm rate.

Key words : network intrusion detection；Convolutional Neural Networks(CNN),；Long-Short Term Memory(LSTM),；deep learning