Dueling Q-Learning Model Achieves 99.68% Accuracy in Intrusion Detection

Researchers have developed a novel intrusion detection system (IDS) utilizing a dueling Q-learning model, achieving 99.68% accuracy. This model features a dueling network architecture that separates value and advantage streams, enhancing learning efficiency and stability. The system was trained on the CIC-IDS2018 dataset, which includes various attack types like DDoS and botnets. Additionally, Explainable AI (XAI) techniques, specifically SHAP, were integrated to provide interpretability for the model's predictions. AI

IMPACT This research introduces a more efficient and interpretable method for detecting cyber threats, potentially improving the robustness of security systems against evolving attack vectors.

RANK_REASON The cluster contains an academic paper detailing a novel machine learning approach for intrusion detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• CIC-IDS2018
• Dueling Deep Q-Learning
• Explainable AI
• Intrusion Detection Systems
• SHAP

• paper
• model release

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