Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders
Joint Authors
Source
Security and Communication Networks
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-16
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Yu, Yang& Long, Jun& Cai, Z.. 2017. Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders. Security and Communication Networks،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1202899
Modern Language Association (MLA)
Yu, Yang…[et al.]. Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders. Security and Communication Networks No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1202899
American Medical Association (AMA)
Yu, Yang& Long, Jun& Cai, Z.. Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders. Security and Communication Networks. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1202899
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202899