INTRUSION DETECTION IN CYBERSECURITY: A STUDY ON EXPLAINABLE GRAPHIC REINFORCEMENT LEARNING

Intrusion Detections Systems (IDS), which are consequently vital for safeguarding digital infrastructure, counter evolving cyber threats. Often, conventional IDS systems including signature-based and anomaly-based battle dynamic attack patterns and high false warning rates. Artificial intelligence (AI) driven solutions, especially reinforcement learning (RL) and graph-based models—have grown more popular in reaction to their capacity to adapt and identify sophisticated threats. As a result, the lack of transparency that is associated with AI-driven intrusion detection systems provides a significant challenge for decision-makers in the field of cybersecurity. Growing confidence and interpretability in AI-based intrusion detection have been greatly influenced by explainable artificial intelligence (XAI). Emphasizing their efficacy in modeling network traffic, enhancing detection accuracy, and guaranteeing decision transparency, this paper seeks to investigate the incorporation of explainability in graph-based reinforcement learning models for IDS. Using secondary data gathering from online databases covering the years 2018 to 2025, a qualitative research approach is employed. The study methodically surveys research on explainability methods in AI-driven IDS, graph-based intrusion detection, and reinforcement learning applications in cybersecurity. Though explainability systems increase interpretability with minimal accuracy loss, the results show that graph-based RL improves intrusion detection and network traffic analysis by utilizing structural links. Nevertheless, problems including adversarial assaults, computation costs, and the trade-off between openness and performance remain. The research shows that using explainable artificial intelligence in graph-based RL IDS can significantly increase detection capabilities and user confidence, hence promoting more efficient and responsible cybersecurity solutions, future studies should concentrate on increasing the scalability, durability, and real-time applicability of explainable graph-based RL models in the field of cyber security.