Customers of virtually all online marketplaces rely upon reviews in order to select the product or service they wish to buy. These marketplaces 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.

中文翻译：

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明星

几乎所有在线市场的客户都依赖评论来选择他们想要购买的产品或服务。这些市场反过来部署评论欺诈检测系统，以保持评论的完整性。评论欺诈检测系统的一个众所周知的问题是它们的基本假设，即大多数评论都是诚实的——这种假设导致了一个漏洞，攻击者可以尝试生成许多对产品的虚假评论。在本文中，我们考虑了一家公司希望通过虚假评论来欺诈性地推广其产品的情况，并提出了基于 Sockpuppet 的评论系统目标攻击（简称 STARS）。STARS 使攻击者能够从多个明显独立的 sockpuppet 帐户为产品输入虚假评论。我们表明，STARS 攻击使公司能够成功地在四个数据集（亚马逊、Epinions 以及 BitcoinAlpha 和 OTC 交易所）上针对七个最近知名的评论欺诈检测器成功推广他们的产品，并获得可观的利润。为了防止 STARS 攻击，我们提出了一种新的欺诈检测算法，称为 RTV。RTV 引入了一类新的用户（称为受信任用户），并且还考虑了经过验证的用户留下的评论，这些评论在现有的评论欺诈检测器中没有考虑到。我们表明，RTV 显着减轻了 STARS 攻击对上面列出的四个数据集的影响。我们提出了一种新的欺诈检测算法，称为 RTV。RTV 引入了一类新的用户（称为受信任用户），并且还考虑了经过验证的用户留下的评论，这些评论在现有的评论欺诈检测器中没有考虑到。我们表明，RTV 显着减轻了 STARS 攻击对上面列出的四个数据集的影响。我们提出了一种新的欺诈检测算法，称为 RTV。RTV 引入了一类新的用户（称为受信任用户），并且还考虑了经过验证的用户留下的评论，这些评论在现有的评论欺诈检测器中没有考虑到。我们表明，RTV 显着减轻了 STARS 攻击对上面列出的四个数据集的影响。