OPTIMAL ATTACKING STRATEGY AGAINST ONLINE REPUTATION SYSTEMS WITH CONSIDERATION OF THE MESSAGE-BASED PERSUASION PHENOMENON

The past decades witness the rise and proliferation of online reputation systems. These reputation systems are vulnerable to malicious attacks, and most recent studies have focused on how to better defend the system. This paper aims to analyze the optimal attacking strategy, especially when considering the "message-based persuasion" phenomenon where users' ratings tend to be influenced by earlier ones. Based on a simple model of users' herding behavior in reputation systems, we study how attackers can explore this phenomenon to attack the system more effectively, and quantitatively analyze the optimal attacking strategies. This investigation is critical to the design of defensive mechanisms, and to the protection of online reputation systems.