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This study introduces a dueling deep Q-learning model for intrusion detection systems (IDS) that achieves an impressive average accuracy of 99.68% across various attack classes, addressing the limitations of traditional supervised machine learning methods in adapting to new cyber threats. The model's unique architecture separates value and advantage streams, enhancing both learning efficiency and stability. Additionally, the integration of SHAP for explainability provides insights into the model's decision-making process, making it a robust tool for real-world applications in cybersecurity.
Achieving 99.68% accuracy, this dueling Q-learning model not only outperforms traditional IDS methods but also offers explainability through SHAP, bridging the gap between performance and interpretability in cybersecurity.
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.
