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A Lightweight Trust Mechanism with Attack Detection for IoT.

Xujie Zhou1, Jinchuan Tang1, Shuping Dang2

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This study introduces a lightweight trust mechanism for Internet of Things (IoT) devices, enhancing security against trust attacks. The system provides accurate trust estimations and effectively isolates malicious devices.

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Internet of Thingsattack detectiontrust attacktrust mechanism

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Internet of Things (IoT)

Background:

  • Internet of Things (IoT) devices face trust evaluation challenges due to limited resources and susceptibility to trust attacks.
  • Existing trust mechanisms may suffer from single-source bias and fail to adapt to dynamic device status changes.

Purpose of the Study:

  • To propose a lightweight and adaptable trust mechanism for IoT devices.
  • To address challenges of limited resources and sophisticated trust attacks in IoT environments.
  • To enhance the accuracy and responsiveness of trust estimations.

Main Methods:

  • Utilized Bayesian statistics and Jøsang's belief model for trust quantification, allowing multi-source feedback to mitigate bias.
  • Implemented a forgetting algorithm to ensure trust estimations reflect the latest device status.
  • Developed a 'tango' algorithm and hypothesis testing for trust attack detection and mitigation.

Main Results:

  • The proposed mechanism provides accurate trust estimations even under combined and colluding attacks.
  • The system effectively excludes malicious devices and rapidly restores trust for legitimate devices.
  • The forgetting algorithm automatically adapts parameters for optimal performance, balancing accuracy and complexity.

Conclusions:

  • The developed trust mechanism is robust, lightweight, and adaptable for secure IoT ecosystems.
  • It successfully counters various trust attacks, ensuring reliable trust evaluations in dynamic environments.
  • The approach offers a significant improvement over conventional methods for IoT trust management.