A Hybrid XAI–ARAS Approach for Evaluating Cybersecurity Performance and Threat Detection

The rapid pace of development of digital technologies has led to an exponential increase in cybersecurity challenges. This has necessitated the development of sophisticated and intelligent cybersecurity solutions. Artificial Intelligence (AI) has been identified as an effective means of addressing cybersecurity challenges. However, the lack of transparency of traditional Artificial Intelligence solutions has raised several concerns. This paper proposes Explainable Artificial Intelligence (XAI) as an approach for enhancing transparency and trust. XAI allows security analysts to interpret AI results, enhancing the accuracy of cybersecurity solutions. To assess several cybersecurity parameters, including accuracy, granularity, scalability, and reliability, this paper proposes the application of Multi-Criteria Decision-Making methodologies. The proposed approach is based on the application of Additive Ratio Assessment. This study specifically examines Explainable Artificial Intelligence (XAI) as a tool to promote transparency and trust in cybersecurity systems. Explainable Artificial Intelligence can help security experts comprehend and interpret AI-based decisions, which improves the