Using gaming strategies for attacker and defender in recommender systems

Abstract

Ratings are the prominent factors to decide the fate of any product in the present Internet Market and many people follow the ratings in a genuine sense. Unfortunately, the Sibyl attacks can affect the credibility of the genuine product. Influence limiter algorithms in recommender systems have been used extensively to overcome the Sibyl attacks but the effort could not reach the safe mark. This paper highlights an approach to generating gaming strategies for the attacker and defender in a recommender system. In a given recommender system environment, attackers and defenders play the most crucial part in a gaming strategy. A sequence of decision rules that an attacker or defender may use to achieve their desired goal is represented in these strategies involved in the game theory. The valid approaches to avoid the Sibyl attacks from the attackers are efficiently defended by the defenders. In our approach, we define attack graphs, use cases, and misuses cases in our gaming framework to analyze the vulnerabilities and security measures incorporated in a recommender system.