IEEE Access· 2017Q1
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
- 2,003citations
- Q1SCImago
- 2017year
Short summary
A novel recurrent neural network (RNN-IDS) model significantly outperforms traditional machine learning methods (J48, ANN, RF, SVM) in both binary and multiclass network intrusion detection, achieving higher accuracy.
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Key points
- Introduced RNN-IDS, a deep learning model based on recurrent neural networks for intrusion detection.
- RNN-IDS achieved higher accuracy than J48, artificial neural network, random forest, and support vector machine methods.
- The model's performance was evaluated in both binary and multiclass classification scenarios.
- Investigated the influence of neuron numbers and learning rates on the model's performance.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. IEEE Access, 2017 · DOI ↗
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Field: Computer Networks and Communications
Computer Networks and CommunicationsComputer Science