Analysis of Perceptions and Expectations Regarding the Integration of Explainable AI (XAI) and Federated Learning (FL) for Cyber Threat Detection
Keywords:
Cyber Security, Explainable AI (XAI)Abstract
The application of advanced technologies such as Explainable Artificial Intelligence (XAI) and Federated Learning (FL) has been a key factor in the evolution of cybersecurity threat detection systems. These technologies offer significant potential—XAI improves the transparency and interpretability of AI decisions, while FL enables collaborative model training without compromising user data privacy. However, the success of their implementation heavily depends on the perceptions and trust of stakeholders. This study aims to analyze the factors influencing expectations and trust in XAI/FL-based security systems. A quantitative approach was adopted, involving an online survey of 100 internet users in Indonesia. This study examines the causal relationship model between three core constructs: Perceived Opportunities and Challenges of XAI/FL, Conceptual Understanding of XAI/FL, and Trust and Expectations of XAI/FL Systems. The analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The results of the study indicate that Perceived Opportunities and Challenges have a positive and significant influence on Conceptual Understanding (β = 0.715, p < 0.001). Additionally, Conceptual Understanding (β = 0.409, p = 0.004) and Perceived Opportunities and Challenges (β = 0.381, p = 0.002) significantly and positively influence Trust and Expectations. These findings indicate that Conceptual Understanding acts as a partial mediator, emphasizing that building stakeholder trust requires not only communication of benefits and challenges, but also structured educational efforts to deepen understanding of the technology.
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