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A Deep Learning Approach to Network Intrusion Detection

Shone, N and Tran Nguyen, N and Vu Dinh, P and Shi, Q A Deep Learning Approach to Network Intrusion Detection. IEEE Transactions on Emerging Topics in Computational Intelligence. ISSN 2471-285X (Accepted)

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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.

Item Type: Article
Additional Information: (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: IEEE
Date Deposited: 07 Nov 2017 09:51
Last Modified: 07 Nov 2017 09:51
URI: http://researchonline.ljmu.ac.uk/id/eprint/7479

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