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
Record details
Published: 11 August 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Military & security
Retrieved: 13 August 2026
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ethics.ai (11 August 2026), “Dueling Deep Q-Learning for Intrusion Detection,” evidence record 19059, https://ethics.ai/record/19059 (originally published by arXiv cs.CR (AI security)).
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