SockDef: A Dynamically Adaptive Defense to a Novel Attack on Review Fraud Detection Engines

Youzhi Zhang, Sayak Chakrabarty, Rui Liu, Andrea Pugliese, V. S. Subrahmanian

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Fake reviews are having a devastating negative influence on online shopping sites. The proliferation of fake reviews is exacerbated by the presence of SockFarms, companies that create and operate huge sets of 'sockpuppet' accounts to promote their customers' products by posting fake reviews. Our proposed SockAttack algorithm allows such companies to optimize their actions to maximize profits. We show that SockAttack compromises the F1-score of four well-known review fraud detection engines on real-world datasets (up to 27.1% more than baselines). We then propose a defense algorithm called SockDef and show that it mitigates the impact of SockAttack (up to 69.2% with respect to F1-score).

Original languageEnglish (US)
Pages (from-to)5253-5265
Number of pages13
JournalIEEE Transactions on Computational Social Systems
Volume11
Issue number4
DOIs
StatePublished - 2024

Funding

This work was supported by ONR under Grant N00014-18-1-2670 and Grant N00014-20-1-2407.

Keywords

  • Deep Learning
  • Markov processes
  • electronic commerce
  • fraud

ASJC Scopus subject areas

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction

Fingerprint

Dive into the research topics of 'SockDef: A Dynamically Adaptive Defense to a Novel Attack on Review Fraud Detection Engines'. Together they form a unique fingerprint.

Cite this