An Effective Deep Learning Based Scheme for Network Intrusion Detection
Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose an effective network intrusion detection scheme based on deep learning techniques. The proposed scheme employs a denoising autoencoder (DAE) with a weighted loss f...
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| Veröffentlicht in: | 2018 24th International Conference on Pattern Recognition (ICPR) S. 682 - 687 |
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IEEE
01.08.2018
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| Abstract | Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose an effective network intrusion detection scheme based on deep learning techniques. The proposed scheme employs a denoising autoencoder (DAE) with a weighted loss function for feature selection, which determines a limited number of important features for intrusion detection to reduce feature dimensionality. The selected data is then classified by a compact multilayer perceptron (MLP) for intrusion identification. Extensive experiments are conducted on the UNSW-NB dataset to demonstrate the effectiveness of the proposed scheme. With a small feature selection ratio of 5.9%, the proposed scheme is still able to achieve a superior performance in terms of different evaluation criteria. The strategic selection of a reduced set of features yields satisfactory detection performance with low memory and computing power requirements, making the proposed scheme a promising solution to intrusion detection in high-speed networks. |
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| AbstractList | Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose an effective network intrusion detection scheme based on deep learning techniques. The proposed scheme employs a denoising autoencoder (DAE) with a weighted loss function for feature selection, which determines a limited number of important features for intrusion detection to reduce feature dimensionality. The selected data is then classified by a compact multilayer perceptron (MLP) for intrusion identification. Extensive experiments are conducted on the UNSW-NB dataset to demonstrate the effectiveness of the proposed scheme. With a small feature selection ratio of 5.9%, the proposed scheme is still able to achieve a superior performance in terms of different evaluation criteria. The strategic selection of a reduced set of features yields satisfactory detection performance with low memory and computing power requirements, making the proposed scheme a promising solution to intrusion detection in high-speed networks. |
| Author | Wu, Chase Q. Gao, Shan Xu, Yuxiao Zhang, Hongpo Liu, Yongpeng Wang, Zongmin |
| Author_xml | – sequence: 1 givenname: Hongpo surname: Zhang fullname: Zhang, Hongpo organization: State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China – sequence: 2 givenname: Chase Q. surname: Wu fullname: Wu, Chase Q. organization: Department of Computer Science, New Jersey Institute of Technology, Newark, USA – sequence: 3 givenname: Shan surname: Gao fullname: Gao, Shan organization: Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China – sequence: 4 givenname: Zongmin surname: Wang fullname: Wang, Zongmin organization: Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China – sequence: 5 givenname: Yuxiao surname: Xu fullname: Xu, Yuxiao organization: Department of Research and Development, Hangzhou DPtech Technologies Co., Ltd., Hangzhou, China – sequence: 6 givenname: Yongpeng surname: Liu fullname: Liu, Yongpeng organization: Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China |
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| Snippet | Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose an effective network... |
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| SubjectTerms | deep learning denoising autoen-coder Engines Feature extraction feature selection Hidden Markov models Intrusion detection Intrusion detection system Noise reduction Training |
| Title | An Effective Deep Learning Based Scheme for Network Intrusion Detection |
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