A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
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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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SVM produced a classification report with the highest accuracy rate, which is 95%, followed by DT with 93% and KNN with 92%.
“SVM produced a classification report with the highest accuracy rate, which is 95 %, followed by DT with a rate of 93 % and KNN with a rate of 92 %.”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [261]
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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
- gold
- doi.org
- 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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