Federated Learning and Blockchain for Smart Home Security: A Comprehensive Review

Abstract

Smart homes utilize Internet of Things (IoT) products to increase convenience and automation, but this also makes them vulnerable to various cybersecurity risks. Traditional centralized Intrusion Detection Systems (IDSs) have limitations, such as single points of failure and issues with privacy, scalability, and accuracy. In this review, we discuss the potential of Federated Learning and blockchain for intrusion detection in smart homes, addressing the limitations of traditional IDSs. A Systematic Literature Review (SLR) was conducted to analyze the use of the blockchain framework, consensus algorithms, and methods for enhancing the framework, such as implementing lightweight frameworks, Edge Computing, model compression, and intelligent contract optimization. The findings of the literature review suggest that the proposed framework improves the system's resistance to attacks, the model's accuracy (>90%), and its integrity through the blockchain's tamper-proof mechanism. However, it also presents challenges in scalability and other areas, so the focus should be on using this framework in the real world to meet the needs of the intrusion detection mechanism in a smart home.