Publication

Evaluating Changes to Fake Account Verification Systems

International Symposium on Research in Attacks, Intrusions and Defenses (RAID)


Abstract

Online social networks (OSNs) such as Facebook, Twitter, and LinkedIn give hundreds of millions of individuals around the world the ability to communicate and build communities. However, the extensive user base of OSNs provides considerable opportunity for malicious actors to abuse the system, with fake accounts generating the vast majority of harmful actions and content. Social networks employ sophisticated detection mechanisms based on machine-learning classifiers and graph analysis to identify and remediate the actions of fake accounts. Disabling or deleting these detected accounts is not tractable when the number of false positives (i.e., real users disabled) is significant in absolute terms. Using challenge-based verification systems such as CAPTCHAs or phone confirmation as a response for detected fake accounts can enable erroneously detected real users to recover their access, while also making it difficult for attackers to abuse the platform.

In order to maintain a verification system’s effectiveness over time, it is important to iterate on the system to improve the real user experience and adapt the platform’s response to adversarial actions. However, at present there is no established method to evaluate how effective each iteration is at stopping fake accounts and letting real users through. This paper proposes a method of assessing the effectiveness of experimental iterations for OSN verification systems, and presents an evaluation of this method against human- labelled ground truth data using production Facebook data. Our method reduces the volume of necessary human labelled data by 70%, decreases the time necessary for classification by 81%, has suitable precision/recall for making decisions in response to experiments, and enables continuous monitoring of the effectiveness of the applied experimental changes.

Related Publications

All Publications

Distributed Auditing Proofs of Liabilities

Kostas Chalkias, Kevin Lewi, Payman Mohassel, Valeria Nikolaenko

ZKProof - April 22, 2020

FastPay: High-Performance Byzantine Fault Tolerant Settlement

Mathieu Baudet, George Danezis, Alberto Sonnino

AFT - November 1, 2020

Taming the many EdDSAs

Kostas Chalkias, François Garillot, Valeria Nikolaenko

SSR - October 22, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy