STARS: Defending against Sockpuppet-Based Targeted Attacks on Reviewing Systems

Customers of virtually all online marketplaces rely upon reviews in order to select the product or service they wish to buy. These market places in turn deploy review fraud detection systems so that the integrity of reviews is preserved. A well-known problem with review fraud detection systems is their underlying assumption that the majority of reviews are honest-this assumption leads to a vulnerability where an attacker can try to generate many fake reviews of a product. In this article, we consider the case where a company wishes to fraudulently promote its product through fake reviews and propose the Sockpuppet-based Targeted Attack on Reviewing Systems (STARS for short). STARS enables an attacker to enter fake reviews for a product from multiple, apparently independent, sockpuppet accounts. We show that the STARS attack enables companies to successfully promote their product against seven recent, well-known review fraud detectors on four datasets (Amazon, Epinions, and the BitcoinAlpha and OTC exchanges) by significant margins. To protect against the STARS attack, we propose a new fraud detection algorithm called RTV. RTV introduces a new class of users (called trusted users) and also considers reviews left by verified users which were not considered in existing review fraud detectors. We show that RTV significantly mitigates the impact of the STARS attack across the four datasets listed above.