Decoding Cyber Identity Theft Through the Innovative ShieldCyNet Framework

Cyber identity theft is a pressing concern, particularly in regions like Tamil Nadu, where increasing digital adoption has heightened vulnerabilities. This study introduces the ShieldCyNet framework, a modular and scalable solution for detecting, mitigating, and responding to identity theft incidents. Leveraging machine learning models, including Neural Networks, Random Forests, and XGBoost, the framework achieved remarkable accuracy in threat detection and classification. Neural Networks outperformed other models with an accuracy of 96.5%, precision of 95.8%, recall of 96.0%, and an F1-score of 95.9%. The framework's performance was further validated through metrics like false positives (2.1%) and false negatives (1.8%) for Neural Networks. Additionally, the system's localized design incorporated multilingual support and culturally relevant features, receiving a user satisfaction score of 92% and ease-of-use rating of 9/10. The ShieldCyNet framework demonstrated robust capabilities in identifying patterns such as phishing (45% of attacks) and credential stuffing (25%), which are prevalent in Tamil Nadu. The study concludes that ShieldCyNet is not only effective in addressing existing threats but also scalable for broader applications. Future improvements will focus on integrating blockchain for enhanced security and expanding awareness programs for rural areas.