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Due to the proliferation of IoT and the popularity of smart contracts mediated by blockchain, smart home systems have become capable of providing privacy and security to their occupants. In blockchain-based home automation systems, business logic is handled by smart contracts securely. However, a blockchain-based solution is inherently resource-intensive, making it unsuitable for resource-constrained IoT devices. Moreover, time-sensitive actions are complex to perform in a blockchainbased solution due to the time required to mine a block. In this work, we propose a blockchain-independent smart contract infrastructure suitable for resource-constrained IoT devices. Our proposed method is also capable of executing time-sensitive business logic. As an example of an end-to-end application, we describe a smart camera system using our proposed method, compare this system with an existing blockchain-based solution, and present an empirical evaluation of their performance.
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This content will become publicly available on March 1, 2026
Enhancing Smart Home Security: Blockchain-Enabled Federated Learning with Knowledge Distillation for Intrusion Detection
The increasing adoption of smart home devices has raised significant concerns regarding privacy, security, and vulnerability to cyber threats. This study addresses these challenges by presenting a federated learning framework enhanced with blockchain technology to detect intrusions in smart home environments. The proposed approach combines knowledge distillation and transfer learning to support heterogeneous IoT devices with varying computational capacities, ensuring efficient local training without compromising privacy. Blockchain technology is integrated to provide decentralized, tamper-resistant access control through Role-Based Access Control (RBAC), allowing only authenticated devices to participate in the federated learning process. This combination ensures data confidentiality, system integrity, and trust among devices. This framework’s performance was evaluated using the N-BaIoT dataset, showcasing its ability to detect anomalies caused by botnets such as Mirai and BASHLITE across diverse IoT devices. Results demonstrate significant improvements in intrusion detection accuracy, particularly for resource-constrained devices, while maintaining privacy and adaptability in dynamic smart home environments. These findings highlight the potential of this blockchain-enhanced federated learning system to offer a scalable, robust, and privacy-preserving solution for securing smart homes against evolving threats.
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- PAR ID:
- 10623889
- Publisher / Repository:
- MDPI Smart Cities 2025
- Date Published:
- Journal Name:
- Smart Cities
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2624-6511
- Page Range / eLocation ID:
- 35
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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