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AI-based Approaches for Detecting Rating Manipulation and Fraudulent Behavior in Online Trust Systems: A Comprehensive Review
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 91 |
| Year of Publication: 2026 |
| Authors: Nwosu Levi Anyehechukwu, Nwosu Cynthia Chideraa, Ogbonna Francisca Chinwendu |
10.5120/ijca2026926416
|
Nwosu Levi Anyehechukwu, Nwosu Cynthia Chideraa, Ogbonna Francisca Chinwendu . AI-based Approaches for Detecting Rating Manipulation and Fraudulent Behavior in Online Trust Systems: A Comprehensive Review. International Journal of Computer Applications. 187, 91 ( Mar 2026), 11-18. DOI=10.5120/ijca2026926416
Abstract
The proliferation of e-commerce platforms and online communities has necessitated robust trust mechanisms to facilitate secure transactions among anonymous users. Trust and reputation systems serve as digital surrogates for traditional word-of-mouth recommendations, enabling users to make informed decisions based on aggregated feedback from prior interactions. However, these systems face critical vulnerabilities from malicious users who manipulate ratings through subtle and calculated attacks, including rating inflation, deflation, and collusion. This paper presents a comprehensive review of computational approaches for building trustworthy reputation systems, with emphasis on detecting fraudulent behavior and rating manipulation. The review systematically examines trust computation models, malicious user detection techniques, attack mitigation strategies, and privacy-preserving mechanisms in online communities. The research synthesizes findings spanning Bayesian reputation engines, statistical filtering methods, machine learning algorithms, and cryptographic techniques for secure trust evaluation. Persistent challenges are identified including the cold-start problem, incentive mechanism design, privacy preservation, and robustness against sophisticated attack vectors. Furthermore, emerging trends in artificial intelligence and distributed systems that offer promising directions for developing next-generation trust architectures are discussed. The analysis reveals that while significant progress has been made in trust computation and attack detection, critical gaps remain in creating adaptive systems that balance security, privacy, user motivation, and computational efficiency. This review provides researchers and practitioners with a structured understanding of the current landscape and identifies opportunities for advancing the state-of-the-art in online trust management.
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