Network Intrusion Detection Methods Based on Deep Learning

Title:Network Intrusion Detection Methods Based on Deep Learning

Volume: 15 Issue: 4

Author(s): Xiangwen Li and Shuang Zhang*

Affiliation:

• Data Recovery Key Laboratory of Sichuan Province, College of Computer Science & AI, Neijiang Normal University, Neijiang, Sichuan,China

Keywords: Honeypot, intrusion protection, intrusion detection, deep learning, network security, DNN-LSTM.

Abstract: To detect network attacks more effectively, this study uses Honeypot techniques to collect the latest network attack data and proposes network intrusion detection classification models, based on deep learning, combined with DNN and LSTM models. Experiments showed that the data set training models gave better results than the KDD CUP 99 training model’s detection rate and false positive rate. The DNN-LSTM intrusion detection algorithm, proposed in this study, gives better results than KDD CUP 99 training model. Compared to other algorithms, such as LeNet, DNNLSTM intrusion detection algorithm exhibits shorter classification test time along with better accuracy and recall rate of intrusion detection.

Cite this article as:

Li Xiangwen and Zhang Shuang *, Network Intrusion Detection Methods Based on Deep Learning, Recent Patents on Engineering 2021; 15 (4) : e210421180688 . https://dx.doi.org/10.2174/1872212114999200403092708