An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units

Xu, Congyuan; Shen, Jizhong; Du, Xin; Zhang, Fan

doi:10.1109/access.2018.2867564

Cited by 339Network Security and Intrusion DetectionNetwork Packet Processing and OptimizationArtificial Immune Systems Applications

An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units

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Xu, Congyuan, Shen, Jizhong, Du, Xin, Zhang, Fan·2018·IEEE Access

DOI 10.1109/access.2018.2867564

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Abstract

To improve the performance of network intrusion detection systems (IDS), we applied deep learning theory to intrusion detection and developed a deep network model with automatic feature extraction. In this paper, we consider the characteristics of the time-related intrusion and propose a novel IDS that consists of a recurrent neural network with gated recurrent units (GRU), multilayer perceptron (MLP), and softmax module. Experiments on the well-known KDD 99 and NSL-KDD data sets show that the system has leading performance. The overall detection rate was 99.42% using KDD 99 and 99.31% using NSL-KDD with false positive rates as low as 0.05% and 0.84%, respectively. In particular, for detecting the denial of service attacks, the system achieved detection rates of 99.98% and 99.55%, respectively. Comparative experiments showed that the GRU is more suitable as a memory unit for IDS than LSTM, and proved that it is an effective simplification and improvement of LSTM. Moreover, the bidirectional GRU can reach the best performance compared with the recently published methods.

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The rarer attacks frequently represent outliers.
“Additionally, the rarer attacks frequently represent outliers [254].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [254]

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Authors: Xu, C., Shen, J., Du, X., & Zhang, F.
Affiliations: Zhejiang University (CN)
ORCID: Xu, Congyuan
Shen, Jizhong
Du, Xin
Zhang, Fan
Venue: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2018
Volume: 6
Pages: 48697-48707
ISSN: 2169-3536 (ISSN-L)
DOI: 10.1109/access.2018.2867564
OpenAlex: W2889165715
Cited by: 339
References: 38
Citations by year: 2026: 17 · 2025: 50 · 2024: 63 · 2023: 52 · 2022: 59 · 2021: 49 · 2020: 29 · 2019: 19 · 2018: 1
FWCI: 15.17
Type: journal article
Language: en
Open access: gold
doi.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations: IEEE Access (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Natural Science Foundation of China (61471314, 61432357)
Welfare Technology Research Project of Zhejiang Province (LGG18F010003)
China Scholarship Council (CSC201606325012)
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
Keywords: network intrusion detection, gated recurrent unit, recurrent neural networks, bidirectional GRU, multilayer perceptron, denial-of-service attacks, KDD Cup 1999 dataset, NSL-KDD dataset, LSTM comparison, automatic feature extraction

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An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units

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