Abstract

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.

Keywords

AutoencoderDeep learningArtificial intelligenceComputer scienceMachine learningIntrusion detection systemBenchmark (surveying)Classifier (UML)Graphics processing unitArtificial neural networkGraphicsPattern recognition (psychology)Data mining

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Publication Info

Year
2018
Type
article
Volume
2
Issue
1
Pages
41-50
Citations
1498
Access
Closed

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Cite This

Nathan Shone, Trần Nguyên Ngọc, Phai Vu Dinh et al. (2018). A Deep Learning Approach to Network Intrusion Detection. IEEE Transactions on Emerging Topics in Computational Intelligence , 2 (1) , 41-50. https://doi.org/10.1109/tetci.2017.2772792

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DOI
10.1109/tetci.2017.2772792

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Data completeness: 81%