STRIDE-Based Threat Analysis and AI-Driven Dataset Design for Securing Educational E-Payment Systems - Doddy Ferdiansyah; Leony Lidya; Miftahul Fadli Muttaqin (Universitas Suryakancana Cianjur, 2025)
The increasing adoption of electronic payment (e-payment) systems in educational settings introduces significant cybersecurity challenges. This study conducts a systematic security analysis of a web-based school canteen e-payment system using the STRIDE threat modeling framework. The methodology involves three stages: system modeling with a Data Flow Diagram (DFD), threat mapping across system components, and qualitative risk assessment based on potential impact and likelihood. The analysis identified six STRIDE threat categories, with high-risk findings in Tampering (balance and price manipulation), Spoofing (account takeover), and Denial of Service (flooding attacks). Recommended mitigation strategies include multi-factor authentication, strict server-side input validation, immutable logging, and secure session management. Beyond manual threat analysis, this research contributes by designing a structured threat dataset as a foundation for artificial intelligence (AI) integration. This dataset enables the development of AI models for automated threat classification, risk prediction, and adaptive mitigation recommendations. The findings highlight the importance of proactive and forward-looking security approaches while opening pathways for future research on data-driven security automation in educational digital infrastructures.