Performance Analysis of Block–Internet of Things–Chain (BIoTC) Framework for Smart Home

Abstract

Smart-home systems built on Internet of Things (IoT) devices offer convenience, but they also introduce gaps in security and privacy that are often overlooked. Traditional protective measures such as lightweight encryption, access control rules, and intrusion alerts do help, but they do not fully address issues such as traceability, proof of device identity, or resistance to more adaptive attacks. In response to this, the research proposes a blockchain-supported security model called Block–Internet of Things–Chain (BIoTC), designed specifically for smart-home IoT networks. This work developed the setup using Cisco Packet Tracer to model a basic smart home. In this  environment, the study carried out on four forms of attacks: Man in the Middle (12 attempts), RFID cloning (6 attempts), unauthorized access (1 case), and social engineering (1 case). Each attack was performed on a regular IoT configuration, and then the same test was repeated after introducing the BIoTC layer. The blockchain component, implemented through a lightweight Python module, maintains immutability and coordinates identity checks to confirm that a device should be allowed in the network. The distinct contribution of research comes from grounding the evaluation in actual experimental observation rather than theoretical claims. The results show that BIoTC is successful in the reduction of attack success rate with acceptable performance overhead for a smart-home environment. Using a real-world example, the mathematical validation was performed. This research paper presents verified and statistical methods for the security of IoT smart-home systems. The results demonstrate that integrating blockchains enhances defense against security threats in the IoT environment.



Received: 15 September 2025 | Revised: 12 November 2025 | Accepted: 6 February 2026



Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.


Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.



Author Contribution Statement

Gaurav Vats: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Visualization. Sarvesh Tanwar: Validation, Investigation, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Pankaj Kumar Sharma: Resources, Data curation, Supervision.