Intelligent Risk Assessment in OSNs Using Behavioral Analytics and Trust Metrics

Online Social Networks (OSNs) have become central to digital communication, yet they are increasingly vulnerable to security threats arising from user behavior, content sharing, and trust-based interactions. This paper proposes a behavior-aware security framework that leverages user activity patterns, content sensitivity, interaction frequency, and trust levels to dynamically assess risk and detect malicious behavior. The system architecture incorporates a real-time risk scoring model and an AI-driven behavioral analysis engine, enabling proactive threat identification and user-specific alerts. Simulation results across multiple training epochs demonstrate high detection accuracy (over 93%), low false positive rates, and improved response times. Additionally, the framework effectively engages users through awareness mechanisms and role-based access control. By integrating technical, behavioral, and psychological insights, the proposed model offers a scalable, intelligent solution for enhancing the security and resilience of OSNs in the face of evolving cyber threats. This paper presents a behavior-aware security framework that computes dynamic risk scores based on four core parameters: behavioral pattern score, content sensitivity, friend interaction rate, and trust level. The proposed system was tested on a simulated OSN environment with 500 users and evaluated across 10 training epochs. It achieved a detection accuracy of 93.6%, precision of 91.4%, recall of 92.1%, and an F1-score of 91.7%, with an average detection time of 3.2 seconds and false positive rate of just 5.8%. Over the simulation, 425 out of 450 high-risk users were correctly identified. The model also demonstrated improved learning over time, with risk classification stabilizing after Epoch 5 and detection latency reducing from 4.2 to 3.1 seconds. These results confirm that the framework offers a robust, real-time solution for identifying threats and enhancing security awareness in OSNs, making it suitable for scalable deployment in real-world platforms.