Golden Jackal Driven Optimization for a transparent and interpretable Intrusion Detection System using explainable AI to revolutionize cybersecurity

The increasing connectivity of today’s digital world and contents necessitate robust security solutions to address the escalating range of cyber threats as well as Copyright infringement. Intrusion Detection Systems (IDS) have emerged as essential tools for managing, analyzing, and generating cybersecurity responses against potential cyberattacks. However, IDS face persistent challenges, including detection performance, deployment efficiency, and reliability in real-time scenarios, which remain active areas of research. In addition to these challenges, a novel issue has recently gained attention: the lack of explainability and transparency in IDS predictions. This limitation significantly affects security practitioners’ confidence in system reliability and restricts the practical utilization of the insights produced. To address these concerns, this paper proposes a joint approach to enhance both the performance and explainability of IDS by integrating the Golden Jackal Optimization (GJO) algorithm for cyberattack detection. Furthermore, we incorporate Explainable Artificial Intelligence (XAI) to provide a clear and comprehensive interpretation of the model’s predictions. Notably, the proposed XAI-based model achieved an impressive accuracy of 99.82% and a miss rate of just 0.19%, underscoring its efficiency, trustworthiness, transparency, and interpretability key attributes essential for human operators managing intelligent cybersecurity systems.