Enhancing Cyber Security in IoT Systems via Federated Learning

The integration of the Internet of Things (IoT) in the evolution of electronic grids, smart neighborhoods, and innovative concepts has empowered decision-makers with data utilization and enhanced automation across various domains. Despite these advancements, the subpar cybersecurity standards in connected devices within the hyper-connected environment of IoT networks pose security threats that could jeopardize the daily operations of medical centers, municipalities, and other organizations. To mitigate potential risks, it is imperative to bolster the security features of IoT devices before their integration into broader platforms. This exploration expected to devise an extensive methodology, comprising of seven phases, for executing solidifying tasks to lessen the assault surface. Utilizing the Favored Detailing Materials for Orderly Examinations and meta-investigation strategies, we led a broad writing audit. By taking on a system that tends to security components at every one of the three degrees of IoT plan, we figured out a suggested approach for assessing the security level of an IoT item. To survey the assault surface, a fitted gamble lattice well defined for IoT was created. At last, a technique enveloping reinforcing and weakness evaluation is proposed to decrease the assault surface and raise the wellbeing rating of a Web of Things (IoT) arrangement.