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Security and Communication Networks
Volume 2017, Article ID 4184196, 10 pages
https://doi.org/10.1155/2017/4184196
Research Article

Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders

College of Computer, National University of Defense Technology, Changsha, Hunan 410073, China

Correspondence should be addressed to Zhiping Cai; nc.ude.tdun@iacpz

Received 28 July 2017; Accepted 22 October 2017; Published 16 November 2017

Academic Editor: Wojciech Mazurczyk

Copyright © 2017 Yang Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs). It is quite potential and promising to apply our model in the large-scale and real-world network environments.