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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Veröffentlicht in:IEEE transactions on emerging topics in computational intelligence Jg. 2; H. 1; S. 41 - 50
Hauptverfasser: Shone, Nathan, Ngoc, Tran Nguyen, Phai, Vu Dinh, Shi, Qi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.02.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
ISSN:2471-285X, 2471-285X
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2017.2772792