3 résultats
pour « phishing »
Cet article analyse la montée alarmante de la fraude bancaire numérique aux États-Unis et propose un cadre stratégique pour renforcer la résilience financière. L'auteur préconise une approche intégrée combinant des systèmes d'information avancés, une gestion globale des risques et une gouvernance organisationnelle stricte.
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 goal was to evaluate the proposed warning dialogs with explanations and to compare them with dialogs presented by Chrome, Firefox, and Edge. The study revealed interesting results: most explanations were understandable and familiar to users and were capable of diverting them from visiting malicious sites."