Real-Time Threat Detection and AI-Driven Predictive Security for Consumer Applications

The growing use of artificial intelligence (AI) in consumer applications, especially through smart devices and the Internet of Things (IoT), presents both opportunities and security challenges. Protecting user data and privacy is increasingly critical as these technologies become part of daily life. Traditional security models struggle with the large, real-time data generated by these systems, leading to problems with accuracy, scalability, and threat detection. To address this, we propose a novel framework that combines Collaborative Federated Learning with an edge-cloud architecture for predictive security. Our approach features the Two-Stage Attention Integrated Graph-based Multi-source Spatio-Temporal Data Fusion (2S-AGMSTDF) network, which processes multiple data sources, including sensor inputs, cyber activity, and user behavior patterns. Key components include AKGCN for cyber feature embedding, a GCN-ResNet-based transformer (GRCMT) for spatial data analysis, and an Attention-Based LSTM for temporal feature extraction. By performing local computations at the edge for real-time monitoring and using Federated Averaging at the cloud level, our framework improves accuracy, scalability, and privacy. Real-world evaluations show its superior performance in anomaly detection and predictive security compared to existing methods, offering a robust AI-driven solution for consumer security.