An Efficient Recommendation Filtering-based Trust Model for Securing Internet of Things

2025-08-24

AI Summary

Existing trust mechanisms for IoT dynamic environments suffer from fixed window lengths, susceptibility to false positives during trust abrupt changes, and low-efficiency recommendation filtering. To address these issues, this paper proposes a robust and efficient trust computation model. Methodologically: (1) a dynamic sliding-window mechanism adaptively adjusts the historical observation window; (2) a harmonic mean weighting strategy integrating average trust and temporal decay stabilizes trust score fluctuations; and (3) a personalized subspace clustering algorithm accelerates malicious recommendation filtering. Experimental results demonstrate that the model achieves superior performance in negative-rating attack detection, improving open-close attack identification accuracy by 44% and reducing filtering latency by 95%. Moreover, it maintains strong robustness under high attacker ratios and composite attack scenarios, significantly enhancing both security and real-time responsiveness.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Machine Learning: Learning Preferences or Rankings
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Trust computation is crucial for ensuring the security of the Internet of Things (IoT). However, current trust-based mechanisms for IoT have limitations that impact data security. Sliding window-based trust schemes cannot ensure reliable trust computation due to their inability to select appropriate window lengths. Besides, recent trust scores are emphasized when considering the effect of time on trust. This can cause a sudden change in overall trust score based on recent behavior, potentially misinterpreting an honest service provider as malicious and vice versa. Moreover, clustering mechanisms used to filter recommendations in trust computation often lead to slower results. In this paper, we propose a robust trust model to address these limitations. The proposed approach determines the window length dynamically to guarantee accurate trust computation. It uses the harmonic mean of average trust score and time to prevent sudden fluctuations in trust scores. Additionally, an efficient personalized subspace clustering algorithm is used to exclude recommendations. We present a security analysis demonstrating the resiliency of the proposed scheme against bad-mouthing, ballot-stuffing, and on-off attacks. The proposed scheme demonstrates a competitive performance in detecting bad-mouthing attacks, while outperforming existing works with an approximately 44% improvement in accuracy for detecting on-off attacks. It maintains its effectiveness even when the percentage of on-off attackers increases and in scenarios where multiple attacks occur simultaneously. Additionally, the proposed scheme reduces the recommendation filtering time by 95%.

Problem

• Dynamic window length determination for accurate IoT trust computation
• Harmonic mean method to prevent sudden trust score fluctuations
• Efficient clustering algorithm to reduce recommendation filtering time

Innovation

• Dynamic window length for accurate trust computation
• Harmonic mean to prevent trust score fluctuations
• Efficient personalized subspace clustering for recommendation filtering