The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Deep Learning Approach to Network Intrusion Detection
Open accessAbstract
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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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Deep learning and its applications to machine health monitoring
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894
Deep learning in the automotive industry: Applications and tools
Luckow, A., Cook, M., Ashcraft, N., Weill, E., Djerekarov, E., & Vorster, B. (2016) · 2016 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2016.7841045 · Cited by 164
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Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
Liu, H., & Lang, B. (2019) · Applied Sciences · doi:10.3390/app9204396 · Cited by 1,133
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2866942 · Cited by 700
Application of deep reinforcement learning to intrusion detection for supervised problems
Lopez-Martin, M., Carro, B., & Sanchez-Esguevillas, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112963 · Cited by 360
DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids
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Metadata
- Authors
- Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q.
- Venue
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Published
- 2018-02
- Volume
- 2
- Issue
- 1
- Pages
- 41-50
- ISSN
- 2471-285X (ISSN-L)
- OpenAlex
- W2783741806
- Cited by
- 1,819
- References
- 3
- FWCI
- 94.14
- Type
- journal article
- Language
- en
- Open access
- green
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- Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A Deep Learning Approach to Network Intrusion Detection. IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/tetci.2017.2772792
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