Explainable Artificial Intelligence System for Guiding Companies and Users in Detecting and Fixing Multimedia Web Vulnerabilities on MCS Contexts

Alloza-García, Sergio ; García-Magariño, Iván ; Lacuesta Gilaberte, Raquel (Universidad de Zaragoza)

Resumen: In the evolving landscape of Mobile Crowdsourcing (MCS), ensuring the security and privacy of both stored and transmitted multimedia content has become increasingly challenging. Factors such as human mobility, device heterogeneity, dynamic topologies, and data diversity exacerbate the complexity of addressing these concerns effectively. To tackle these challenges, this paper introduces CSXAI (Crowdsourcing eXplainable Artificial Intelligence)—a novel explainable AI system designed to proactively assess and communicate the security status of multimedia resources downloaded in MCS environments. While CSXAI integrates established attack detection techniques, its primary novelty lies in its synthesis of these methods with a user-centric XAI framework tailored for the specific challenges of MCS frameworks. CSXAI intelligently analyzes potential vulnerabilities and threat scenarios by evaluating website context, attack impact, and user-specific characteristics. The current implementation focuses on the detection and explanation of three major web vulnerability classes: Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), and insecure File Upload. The proposed system not only detects digital threats in advance but also adapts its explanations to suit both technical and non-technical users, thereby enabling informed decision-making before users access potentially harmful content. Furthermore, the system offers actionable security recommendations through clear, tailored explanations, enhancing users’ ability to implement protective measures across diverse devices. The results from real-world testing suggest a notable improvement in users’ ability to understand and mitigate security risks in MCS environments. By combining proactive vulnerability detection with user-adaptive, explainable feedback, the CSXAI framework shows promise in empowering users to enhance their security posture effectively, even with minimal cybersecurity expertise. These findings underscore the potential of CSXAI as a reliable and accessible solution for tackling cybersecurity challenges in dynamic, multimedia-driven ecosystems. Quantitative results showed high user satisfaction and interpretability (SUS = 79.75 ± 6.40; USE subscales = 5.32–5.88).

Idioma: Inglés

DOI: 10.3390/fi17110524

Año: 2025

Publicado en: FUTURE INTERNET 17, 11 (2025), 524 [31 pp]
ISSN: 1999-5903

Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2022-136779OB-C31

Tipo y forma: Article (Published version)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)