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<p>The social internet of things (SIoT) represents an evolution of the internet of things (IoT), where smart devices operate as social entities, enhancing interactions and collaborations. However, ensuring trustworthiness among these devices poses significant challenges. This study introduces a decentralized incremental trust model designed to identify malicious nodes exhibiting dynamic behaviors, thereby improving the security and reliability of the SIoT ecosystem. The model employs incremental machine learning (ML) techniques to analyze device behavior and utilizes a fuzzy logic approach to evaluate service quality, taking into account both intention and capability. Additionally, our proposed model addresses the resource limitations of IoT devices by leveraging the benefits of a hybrid architecture. Validation through experiments on a simulated SIoT network demonstrates the model’s effectiveness in mitigating dynamic malicious activities, resulting in a significant improvement in attack detection rates.</p>

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Enhancing security and trustworthiness in the social internet of things through incremental trust modeling

  • 期刊论文

  • 发表日期：

    2025年11月9日

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  • Elham Moeinaddini,
  • Eslam Nazemi,
  • Amin Shahraki

摘要

The social internet of things (SIoT) represents an evolution of the internet of things (IoT), where smart devices operate as social entities, enhancing interactions and collaborations. However, ensuring trustworthiness among these devices poses significant challenges. This study introduces a decentralized incremental trust model designed to identify malicious nodes exhibiting dynamic behaviors, thereby improving the security and reliability of the SIoT ecosystem. The model employs incremental machine learning (ML) techniques to analyze device behavior and utilizes a fuzzy logic approach to evaluate service quality, taking into account both intention and capability. Additionally, our proposed model addresses the resource limitations of IoT devices by leveraging the benefits of a hybrid architecture. Validation through experiments on a simulated SIoT network demonstrates the model’s effectiveness in mitigating dynamic malicious activities, resulting in a significant improvement in attack detection rates.