DL-IDS: Extracting Features Using CNN-LSTM Hybrid Network for Intrusion Detection System
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- Pengfei Sun
- School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
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- Pengju Liu
- School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
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- Qi Li
- Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing 100084, China
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- Chenxi Liu
- School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
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- Xiangling Lu
- School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
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- Ruochen Hao
- School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
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- Jinpeng Chen
- School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
説明
<jats:p>Many studies utilized machine learning schemes to improve network intrusion detection systems recently. Most of the research is based on manually extracted features, but this approach not only requires a lot of labor costs but also loses a lot of information in the original data, resulting in low judgment accuracy and cannot be deployed in actual situations. This paper develops a DL-IDS (deep learning-based intrusion detection system), which uses the hybrid network of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract the spatial and temporal features of network traffic data and to provide a better intrusion detection system. To reduce the influence of an unbalanced number of samples of different attack types in model training samples on model performance, DL-IDS used a category weight optimization method to improve the robustness. Finally, DL-IDS is tested on CICIDS2017, a reliable intrusion detection dataset that covers all the common, updated intrusions and cyberattacks. In the multiclassification test, DL-IDS reached 98.67% in overall accuracy, and the accuracy of each attack type was above 99.50%.</jats:p>
収録刊行物
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- Security and Communication Networks
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Security and Communication Networks 2020 1-11, 2020-08-28
Hindawi Limited
