期刊
ICT EXPRESS
卷 7, 期 1, 页码 81-87出版社
ELSEVIER
DOI: 10.1016/j.icte.2020.03.002
关键词
Recurrent Neural Networks; Machine learning; Intrusion Detection Systems; Deep learning
资金
- South African National Research Foundation [112108, 112142]
- South African National Research Foundation Incentive Grant [95687]
- Eskom Tertiary Education Support Programme Grant, South Africa
- URC of University of Johannesburg, South Africa
This paper introduces a method for implementing IDS based on DGRU, and evaluates its performance using the NSL-KDD benchmark dataset. Experimental results demonstrate a significant performance improvement of DGRU IDS compared to existing methods.
With the advances and growth of various wireless technologies, it is imperative to implement robust Intrusion Detection Systems (IDS). This paper proposes the implementation of Deep Gated Recurrent Unit (DGRU) Based classifier as well as a wrapper-based feature extraction algorithm for Wireless IDS. We assess the performance of the DRGU IDS using the NSL-KDD benchmark dataset. Furthermore, we compare our framework to several popular algorithms including Artificial Neural Networks, Deep Long-Short Term Memory, Random Forest, Naive Bayes and Feed Forward Deep Neural Networks. The experimental outcomes demonstrate that the DGRU IDS displays a significant increase in performance over existing methods. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.
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