An Analysis of Explainable Artificial Intelligence for Intelligent Cybersecurity Applications

Abstract

Proactive cyber defenses, AI-powered threat detection systems, and automated reactions are changing the face of cybersecurity in the modern era. In security-critical applications, however, Explainable Artificial Intelligence (XAI) is in high demand due to the need to improve trust, transparency, and decision-making in light of the fact that traditional AI models are not transparent. Intelligent threat detection and response mechanisms, key cybersecurity applications, the most recent advances in the area of XAI-based security solutions, and the fundamentals of explainable AI are all covered in detail in this survey. Highlighting the most prominent explainability methods including SHAP, LIME, Grad-CAM, and counterfactual explanations, this article delves into their applications in several security domains, including cloud security, IoT security, fraud detection, intrusion detection, phishing, spam, cloud security, and identity and access management. The comparative analysis of recent studies is used to emphasize current accomplishments, problems, and new research areas. The results show that XAI can improve the transparency, trustworthiness and effectiveness of AI-based cybersecurity systems, in addition to highlighting a range of privacy, adversarial robustness, scalability and evaluation challenges that warrant further research to ensure reliable deployment in the real world.