Unleashing the Power of Indirect Attacks against Trust Prediction via Preferential Path

Abstract

In the realm of network security, adversarial attacks pose a significant threat, prompting research into innovative strategies for enhancing both attack and defense mechanisms. This paper presents a novel exploration into the enhancement of adversarial attacks on Fairness and Goodness Algorithm (FGA) and Review to Reviewer (REV2), focusing on trust prediction within signed graphs. In contrast to traditional time-based dynamics, the trust propagation in FGA and REV2 is grounded on iterative processes. Through meticulous analysis of network structures, this research uncovers the strong ties and weak ties within FGA. Additionally , it reveals the existence of preferential paths in REV2, which significantly influence the spread of information during algorithm iterations. Leveraging these revelations, we introduce a novel approach known as vicinage-attack designed to bolster adversarial attacks by strategically targeting edges along these influential pathways. The significance of this work lies in identifying adversarial perturbation patterns that impact trust prediction on signed graphs, shedding light on their pervasive influence. Our findings not only contribute to the advancement of adver-sarial attack techniques but also lay the foundation for a deeper understanding of trust propagation patterns. By elucidating the propagation bias within FGA and REV2, this research offers new insights for both network security strategies and adversarial mitigation techniques in trust prediction.