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. Recurrent neural networks, RNN-IDS, intrusion detection, deep learning, machine learning.
In addition, another work in REF introduced a deep learning approach based on a simple Recurrent Neural Networks (RNN) with forward and back propagations, for network intrusion detection.
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
IEEE Access
IEEE Access
Computer Science