Cited by 2,100Network Security and Intrusion DetectionNetwork Packet Processing and OptimizationArtificial Immune Systems Applications

A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

Open access
Yin, Chuanlong, Zhu, Yuefei, Fei, Jinlong, He, Xinzheng·2017·IEEE Access

Abstract

Intrusion detection plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. In this paper, we explore how to model an intrusion detection system based on deep learning, and we propose a deep learning approach for intrusion detection using recurrent neural networks (RNN-IDS). Moreover, we study the performance of the model in binary classification and multiclass classification, and the number of neurons and different learning rate impacts on the performance of the proposed model. We compare it with those of J48, artificial neural network, random forest, support vector machine, and other machine learning methods proposed by previous researchers on the benchmark data set. The experimental results show that RNN-IDS is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of traditional machine learning classification methods in both binary and multiclass classification. The RNN-IDS model improves the accuracy of the intrusion detection and provides a new research method for intrusion detection.

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Crossref · 10.1109/access.2017.2762418

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Authors
Yin, C., Zhu, Y., Fei, J., & He, X.
Venue
IEEE Access
Published
2017
Volume
5
Pages
21954-21961
ISSN
2169-3536 (ISSN-L)
OpenAlex
W2762776925
Cited by
2,100
References
24
Citations by year
2026: 245 · 2025: 281 · 2024: 231 · 2023: 255 · 2022: 258 · 2021: 336 · 2020: 278 · 2019: 158 · 2018: 58
FWCI
89.97
Type
journal article
Language
en
Open access
Locations
  • IEEE Access (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Key Research and Development Program of China (2016YFB0801601, 2016YFB0801505)
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
intrusion detection, recurrent neural networks, network attack classification, multi-class classification, binary classification, network security, benchmark dataset, hyperparameter comparison

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