Building Auto-Encoder Intrusion Detection System based on random forest feature selection
Machine learning techniques have been widely used in intrusion detection for many years. However, these techniques are still suffer from lack of labeled dataset, heavy overhead and low accuracy. To improve classification accuracy and reduce training time, this paper proposes an effective deep learni...
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| Veröffentlicht in: | Computers & security Jg. 95; S. 101851 - 15 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
Elsevier Ltd
01.08.2020
Elsevier Sequoia S.A |
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| ISSN: | 0167-4048, 1872-6208 |
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| Abstract | Machine learning techniques have been widely used in intrusion detection for many years. However, these techniques are still suffer from lack of labeled dataset, heavy overhead and low accuracy. To improve classification accuracy and reduce training time, this paper proposes an effective deep learning method, namely AE-IDS (Auto-Encoder Intrusion Detection System) based on random forest algorithm. This method constructs the training set with feature selection and feature grouping. After training, the model can predict the results with auto-encoder, which greatly reduces the detection time and effectively improves the prediction accuracy. The experimental results show that the proposed method is superior to traditional machine learning based intrusion detection methods in terms of easy training, strong adaptability, and high detection accuracy. |
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| AbstractList | Machine learning techniques have been widely used in intrusion detection for many years. However, these techniques are still suffer from lack of labeled dataset, heavy overhead and low accuracy. To improve classification accuracy and reduce training time, this paper proposes an effective deep learning method, namely AE-IDS (Auto-Encoder Intrusion Detection System) based on random forest algorithm. This method constructs the training set with feature selection and feature grouping. After training, the model can predict the results with auto-encoder, which greatly reduces the detection time and effectively improves the prediction accuracy. The experimental results show that the proposed method is superior to traditional machine learning based intrusion detection methods in terms of easy training, strong adaptability, and high detection accuracy. |
| ArticleNumber | 101851 |
| Author | Chen, Wei Wu, Lifa Li, XuKui Zhang, Qianru |
| Author_xml | – sequence: 1 givenname: XuKui surname: Li fullname: Li, XuKui organization: Nanjing University of Posts and Telecommunications, No.9,Wenyuan Road, Nanjing, Jiangsu, China – sequence: 2 givenname: Wei orcidid: 0000-0002-6248-1298 surname: Chen fullname: Chen, Wei email: chenwei@njupt.edu.cn organization: Nanjing University of Posts and Telecommunications, No.9,Wenyuan Road, Nanjing, Jiangsu, China – sequence: 3 givenname: Qianru surname: Zhang fullname: Zhang, Qianru organization: University of Hong Kong, Pokfulam Road, Central and Western District, Hong Kong, China – sequence: 4 givenname: Lifa surname: Wu fullname: Wu, Lifa organization: Nanjing University of Posts and Telecommunications, No.9,Wenyuan Road, Nanjing, Jiangsu, China |
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| Keywords | Deep learning Network security Network Intrusion Detection System Auto-Encoder Unsupervised clustering |
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| SubjectTerms | Accuracy Algorithms Auto-Encoder Classification Coders Deep learning Feature selection Intrusion Intrusion detection systems Machine learning Network Intrusion Detection System Network security Training Unsupervised clustering |
| Title | Building Auto-Encoder Intrusion Detection System based on random forest feature selection |
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