Work

Automated Crowdturfing Attacks and Defenses in Online Review Systems

Year: 2017

Type: conference-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.

Source: ACM SIGSAC Conference on Computer and Communications Security (CCS)

DOI: 10.1145/3133956.3133990

Authors: Yuanshun Yao, Bimal Viswanath, Jenna Cryan, Haitao Zheng, Ben Y. Zhao

Institution: University of Chicago

Language: English

Topic: Spam and Phishing Detection

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: SaTC: CORE: Medium: Collaborative: Defending against Compromise and Manipulation of Mobile Communities, TWC: Small: User Behavior Modeling and Prediction in Anonymous Social Networks