Attack and Anomaly Detection in IoT Devices using Federated Learning

Abstract

There has been a lot of focus from governments, universities, and businesses in recent years on the intersection of cybersecurity and machine learning (ML) for the Internet of Things (IoT). The Internet of Things (IoT) can be made more secure and efficient in the future through the groundbreaking concept of federated cybersecurity (FC). This new idea has the ability to efficiently identify security problems, implement countermeasures, and contain them within the IoT network infrastructure. Cybersecurity goals are met through the federation of a shared and learned model among several actors. Protecting the insecure IoT environment requires privacy-aware ML models like federated learning (FL).