Nos derniers articles

This research presents a machine learning framework designed to predict and reduce the risk of identity theft caused by phishing and social engineering. The authors developed a Cyber Risk Score (CRS) that combines observable security habits, like password hygiene, with latent psychological traits such as impulsive link-clicking. By utilizing a hybrid stacking ensemble model, the study achieved a 93% accuracy rate in identifying vulnerable social media users. Beyond mere prediction, the system uses SHAP analysis to provide transparent, personalized recommendations tailored to an individual’s specific behavioral weaknesses. This user-centered approach aims to bridge the gap between cybersecurity knowledge and actual online behavior through evidence-based interventions. Ultimately, the framework offers a scalable, ethical solution for organizations to protect users in increasingly sophisticated digital environments.

The EBA report highlights payment fraud, driven by social engineering circumventing security, as the top concern for EU consumers. Rising indebtedness due to "Buy-Now-Pay-Later" schemes and poor lending practices is the second key issue. Thirdly, unwarranted de-risking limits vulnerable consumers' access to essential payment accounts. The EBA will consider actions in 2025/26 to address these issues and enhance EU consumer protection.