A Decentralized Federated Learning Architecture for Intrusion Detection in IoT Systems

Abstract

Internet of Things (IoT) systems are vulnerable to several attacks, mainly due to the weakness of IoT devices, which have little computational and memory power, necessary for more sophisticated security features. In addition, IoT systems are distributed systems and thus inherit all problems related to the need to guarantee confidentiality, integrity, and availability. One of the traditional strategies to deal with these problems involves intrusion detection and prevention techniques. It is usual to implement them in a centralized way. In addition to not being scalable for IoT systems with an increasing number of devices, it implies an unacceptable single point of failure. Besides, sending all collected data to a centralized server in the cloud poses a great risk to the privacy of information. This paper presents a decentralized architecture for intrusion detection in IoT-based systems, which is based on federated machine learning, combined with distributed ledger technologies for access control, allowing a mechanism to minimize security risks.