Dueling Deep Q-Learning for Intrusion Detection
Intrusion detection systems (IDS) and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods. Though effective, these models struggle to effectively adapt to new attack types. This study proposes a novel approach by employing a reward-based, dueling Q-learning model for IDS, achieving an average accuracy of 99.68% across multiple attack classes. The proposed model has a dueling network architecture which separates its predictions into value and advantage streams. This has the benefit of improving learning efficiency and stability. The model was trained on the CIC-IDS2018, a benchmark dataset based on real-world intrusion d...
| Authors | Logan Luna, Matthew P. Berkowitz, Laxima Niure Kandel et al. |
|---|---|
| Primary category | cs.CR |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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