mlsanityData to 14 Oct 2026

Dueling Deep Q-Learning for Intrusion Detection

arXiv 2608.11291 ↗First posted 11 Aug 2026cs.CR cs.LGcited by 0

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Abstract (arXiv)

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 detection scenarios, having multiple attack classes such as DDoS, botnets, and brute-force attacks. Furthermore, Explainable AI (XAI), specifically SHAP (SHapley Additive exPlanations), was also integrated into the training and evaluation process to provide interpretability into the model's predictions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 16 references that are in the map. First posted on arXiv 11 Aug 2026.

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4 of 16 references.

References · 4

  1. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  2. 2023Explaining Reinforcement Learning with Shapley Valuescited by 26
  3. 2024Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defensecited by 17
  4. 2021Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detectioncited by 8

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  1. 2024Multi-agent Reinforcement Learning-based Network Intrusion Detection System
  2. 2026Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation
  3. 2026From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation
  4. 2024A Survey for Deep Reinforcement Learning Based Network Intrusion Detection
  5. 2026XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles
  6. 2025A Novel and Practical Universal Adversarial Perturbations against Deep Reinforcement Learning based Intrusion Detection Systems
  7. 2023Deep Neural Networks based Meta-Learning for Network Intrusion Detection
  8. 2025LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity