Automated Crowdturfing Attacks and Defenses in Online Review Systems
Yuanshun Yao
Bimal Viswanath
Jenna Cryan
Haitao Zheng
Ben Y. Zhao
Proceedings of the ACM Conference on Computer and Communications Security (CCS 2017)
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Paper Abstract
Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect.
Using Yelp reviews as an example platform, we show how a two
phased review generation and customization attack can produce
reviews that are indistinguishable by state-of-the-art statistical
detectors. We conduct a survey-based user study to show these
reviews not only evade human detection, but also score high on
"usefulness" metrics by users. Finally, we develop novel automated
defenses against these attacks, by leveraging the lossy transformation
introduced by the RNN training and generation cycle. We
consider countermeasures against our mechanisms, show that they
produce unattractive cost-benefit tradeoffs for attackers, and that
they can be further curtailed by simple constraints imposed by
online service providers.