Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection

TL;DR

This paper introduces a Deep Q-Learning model for network intrusion detection that autonomously learns to identify various cyber threats, outperforming existing machine learning methods on the NSL-KDD dataset.

Contribution

It combines deep neural networks with Q-learning to create an autonomous, self-improving intrusion detection system with optimized hyperparameters.

Findings

DQL outperforms other machine learning approaches in intrusion detection.

Lower discount factor (0.001) with 250 episodes yields optimal results.

The model effectively detects multiple intrusion types.

Abstract

The rise of the new generation of cyber threats demands more sophisticated and intelligent cyber defense solutions equipped with autonomous agents capable of learning to make decisions without the knowledge of human experts. Several reinforcement learning methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a new generation of network intrusion detection methods that combines a Q-learning-based reinforcement learning with a deep-feed forward neural network method for network intrusion detection. Our proposed Deep Q-Learning (DQL) model provides an ongoing auto-learning capability for a network environment that can detect different types of network intrusions using an automated trial-error approach and continuously enhance its detection capabilities. We provide the details of fine-tuning different hyperparameters involved in the DQL model for more effective self-learning. According to our extensive experimental results based on the NSL-KDD dataset, we confirm that the lower discount factor which is set as 0.001 under 250 episodes of training yields the best performance results. Our experimental results also show that our proposed DQL is highly effective in detecting different intrusion classes and outperforms other similar machine learning approaches.