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<p>With the growing adoption of cloud computing, the frequency and complexity of attacks on cloud environments are also increasing. Cloud computing facilitates consumer services across various network infrastructures, including the Internet of Things (IoT). Therefore, monitoring consumer device behaviour is crucial for both consumers and service providers. This work proposes a trust management algorithm to evaluate consumer credibility in cloud-IoT systems. The algorithm calculates a trust value based on cloud user attributes and consumer logs to assess credibility in a multi-cloud environment. Detection accuracy, false positives, and false alarms (in percentage) are used as performance metrics to evaluate credibility. The results show that the proposed algorithm outperforms existing algorithms, namely Saeed scheme, fuzzy logic-based user behaviour trust (FUBT), and Chen scheme, with a detection accuracy of 87.40%, a false positive rate of 12.50%, and zero false alarms. The work is implemented using statistical analysis system 9.4, Amazon Web Service (AWS), and Python.</p>

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A novel algorithm for consumer credibility estimation in cloud-internet of things systems

  • 期刊论文

  • 发表日期：

    2025年7月13日

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  • Kalyan Kumar Jena,
  • Sourav Kumar Bhoi,
  • Sanjaya Kumar Panda,
  • Rajendra Prasad Nayak

摘要

With the growing adoption of cloud computing, the frequency and complexity of attacks on cloud environments are also increasing. Cloud computing facilitates consumer services across various network infrastructures, including the Internet of Things (IoT). Therefore, monitoring consumer device behaviour is crucial for both consumers and service providers. This work proposes a trust management algorithm to evaluate consumer credibility in cloud-IoT systems. The algorithm calculates a trust value based on cloud user attributes and consumer logs to assess credibility in a multi-cloud environment. Detection accuracy, false positives, and false alarms (in percentage) are used as performance metrics to evaluate credibility. The results show that the proposed algorithm outperforms existing algorithms, namely Saeed scheme, fuzzy logic-based user behaviour trust (FUBT), and Chen scheme, with a detection accuracy of 87.40%, a false positive rate of 12.50%, and zero false alarms. The work is implemented using statistical analysis system 9.4, Amazon Web Service (AWS), and Python.