A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis.

Area of Science:

• * Cybersecurity
• * Network Security
• * Internet of Things (IoT)

Background:

• * Existing trust management systems in IoT environments often rely on direct trust ratings, making them vulnerable to malicious data.
• * Security threats in smart city IoT deployments necessitate robust trust evaluation mechanisms.

Purpose of the Study:

• * To propose an Edge-based Trust Management System (E-TMS) for secure IoT environments.
• * To enhance trust computation by eliminating false contributions from malicious devices.
• * To improve the accuracy of identifying compromised IoT devices.

Main Methods:

• * Implementation of an edge-assisted, two-level trust computation approach.
• * Utilization of Eigenvector properties for trust analysis and identification of compromised devices.
• * Generation of a spectral matrix to scale trust values based on device contributions.

Main Results:

• * E-TMS effectively identifies and removes false contributions from aggregated trust data.
• * Eigenvector analysis accurately distinguishes between legitimate and malicious IoT devices, avoiding false detections.
• * The system achieves higher throughput and network resilience compared to existing methods.

Conclusions:

• * The proposed E-TMS provides a reliable method for secure trust evaluation in IoT smart cities.
• * Edge computing integration enhances the accuracy and efficiency of trust management.
• * E-TMS demonstrates superior performance in securing IoT networks against malicious attacks.