Semantic Scholar Open Access 2017 1580 sitasi

A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

Chuanlong Yin Yuefei Zhu Jin-long Fei Xin-Zheng He

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (4)

Chuanlong Yin

Yuefei Zhu

Jin-long Fei

Xin-Zheng He

Format Sitasi

APA MLA BibTeX

Yin, C., Zhu, Y., Fei, J., He, X. (2017). A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks. https://doi.org/10.1109/ACCESS.2017.2762418

Akses Cepat

Lihat di Sumber doi.org/10.1109/ACCESS.2017.2762418

Informasi Jurnal

Tahun Terbit: 2017
Bahasa: en
Total Sitasi: 1580×
Sumber Database: Semantic Scholar
DOI: 10.1109/ACCESS.2017.2762418
Akses: Open Access ✓