A Deep Learning Approach to Network Intrusion Detection
Network intrusion detection systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing le...
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| Published in: | IEEE transactions on emerging topics in computational intelligence Vol. 2; no. 1; pp. 41 - 50 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Piscataway
IEEE
01.02.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 2471-285X, 2471-285X |
| Online Access: | Get full text |
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| Abstract | Network intrusion detection systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in graphics processing unit (GPU)-enabled TensorFlow and evaluated using the benchmark KDD Cup '99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs. |
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| AbstractList | Network intrusion detection systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in graphics processing unit (GPU)-enabled TensorFlow and evaluated using the benchmark KDD Cup '99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs. |
| Author | Shone, Nathan Shi, Qi Phai, Vu Dinh Ngoc, Tran Nguyen |
| Author_xml | – sequence: 1 givenname: Nathan orcidid: 0000-0002-7920-9434 surname: Shone fullname: Shone, Nathan email: n.shone@ljmu.ac.uk organization: Department of Computer Science, Liverpool John Moores University, Liverpool, U.K – sequence: 2 givenname: Tran Nguyen surname: Ngoc fullname: Ngoc, Tran Nguyen email: ngoctn@mta.edu.vn organization: Hanoi 100000, Vietnam – sequence: 3 givenname: Vu Dinh orcidid: 0000-0001-5000-258X surname: Phai fullname: Phai, Vu Dinh email: dinhphai88@gmail.com organization: Hanoi 100000, Vietnam – sequence: 4 givenname: Qi orcidid: 0000-0002-3766-7808 surname: Shi fullname: Shi, Qi email: q.shi@ljmu.ac.uk organization: Department of Computer Science, Liverpool John Moores University, Liverpool, U.K |
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| CODEN | ITETCU |
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| SubjectTerms | Anomaly detection auto-encoders Communication networks Computer networks Deep learning Graphics processing units Intrusion detection Intrusion detection systems KDD Machine learning Monitoring network security Training data |
| Title | A Deep Learning Approach to Network Intrusion Detection |
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