An Efficient Neural Network by Safe and Secured IoT for Smart Device Protection Using Lightweight AI and Edge Security

The rapid expansion of smart gadgets and the Internet of Things (IoT) has created important privacy and security issues that call for sophisticated protective systems. Many times, conventional security measures depend on cloud-based processing, which might cause more latency, high computational expenses, and possible data vulnerabilities. This work offers an effective neural network-based security system using lightweight artificial intelligence (AI) and edge security to protect IoT ecosystems, hence overcoming these constraints. While reducing resource use, the suggested paradigm improves device authentication, anomaly detection, and real-time threat detection. Processing security risks at the edge helps the system to rely less on cloud infrastructure, hence guaranteeing quicker responses and better data privacy. Experimental tests show how well the model reduces cyber risks, protects smart devices, and maximizes resource use. This work helps to provide strong, low-power, smart security systems specifically designed for IoT uses.