Integrate Federated Learning and Edge Computing to Provide Real-Time Attack Prediction with Cybersecurity Threat Intelligence in the Internet of Things

The Internet of Things (IoT) has grown a lot in a short amount of time. This has made the digital ecosystem much bigger and made it easier for devices, sensors, and cloudbased programs to communicate with each other. This hyperconnectivity makes cybersecurity harder because IoT devices typically don't have enough processing power, don't have good means to prove who they are, and use various protocols. Because of latency, scalability challenges, and the fact that they all depend on one point of failure, traditional centralized cybersecurity solutions have a hard time dealing with these problems. This study shows a new system that combines Federated Learning (FL) with Edge Computing to forecast attacks in real time, with the help of Cybersecurity Threat Intelligence (CTI). The suggested solution uses the federated learning distributed framework, which lets IoT devices work together to make predictive models without having to share raw data. This protects privacy and follows the rules for protecting data. Edge computing resources are used to do analytics with no delay and quickly look at data that is close to the sources. This means that fewer centralized cloud infrastructures are needed. introducing CTI to the framework makes it better by introducing systems that can discover hazards before they happen and deal with them in the best way possible. Tests demonstrate that these models are better than most cloud-based ones at finding dangers, acting swiftly, and staying strong. Using robust aggregation methods in the federated learning process makes the strategy even less likely to lead to model poisoning and attacks from enemies. This study emphasizes the imperative for a decentralized, intelligence-driven cybersecurity paradigm that aligns with the escalating complexity of IoT networks. The findings indicate that integrating federated learning with edge-based threat intelligence constitutes a scalable, efficient, and privacypreserving method for predicting assaults. These kinds of changes can aid critical areas like healthcare, smart cities, the Internet of Things (IoT) in businesses, and cars that can connect to the internet. To keep things functioning and keep consumers safe, it's important to find threats in real time. The study establishes the groundwork for a smart and adaptable cybersecurity framework that fulfills the urgent needs of IoT security in a world that is continually evolving.