Unleashing the power of indirect attacks against trust prediction via preferential path

Yu Bu, Yulin Zhu, Longling Geng, Kai Zhou

Abstract: Adversarial attacks in network security are a growing concern, prompting the need for innovative strategies to enhance both attack and defense mechanisms. This paper explores ways to improve adversarial attacks on the fairness and goodness algorithm (FGA) and review to reviewer (REV2), focusing on predicting trust within signed graphs. Unlike traditional time-based models, FGA and REV2 rely on iterative processes for trust propagation. By analyzing network structures, we identify strong ties and weak ties within FGA and discover preferential paths in REV2 that significantly impact information spread during algorithm iterations. Based on these insights, we propose a new approach called the vicinage attack, which enhances adversarial attacks by strategically targeting edges along these critical pathways. Our work highlights adversarial perturbation patterns that affect trust prediction on signed graphs and emphasizes their wide-reaching impact. These findings not only advance adversarial attack techniques but also deepen our understanding of trust propagation patterns. By clarifying the propagation bias in FGA and REV2, this research provides valuable insights for improving network security and developing better adversarial mitigation techniques in trust prediction.