NDLI: Detecting Clusters of Fake Accounts in Online Social Networks

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Detecting Clusters of Fake Accounts in Online Social Networks

Detecting Clusters of Fake Accounts in Online Social Networks

Content Provider	ACM Digital Library
Author	Xiao, Cao Freeman, David Mandell Hwa, Theodore
Abstract	Fake accounts are a preferred means for malicious users of online social networks to send spam, commit fraud, or otherwise abuse the system. A single malicious actor may create dozens to thousands of fake accounts in order to scale their operation to reach the maximum number of legitimate members. Detecting and taking action on these accounts as quickly as possible is imperative in order to protect legitimate members and maintain the trustworthiness of the network. However, any individual fake account may appear to be legitimate on first inspection, for example by having a real-sounding name or a believable profile. In this work we describe a scalable approach to finding groups of fake accounts registered by the same actor. The main technique is a supervised machine learning pipeline for classifying {\em an entire cluster} of accounts as malicious or legitimate. The key features used in the model are statistics on fields of user-generated text such as name, email address, company or university; these include both frequencies of patterns {\em within} the cluster (e.g., do all of the emails share a common letter/digit pattern) and comparison of text frequencies across the entire user base (e.g., are all of the names rare?). We apply our framework to analyze account data on LinkedIn grouped by registration IP address and registration date. Our model achieved AUC 0.98 on a held-out test set and AUC 0.95 on out-of-sample testing data. The model has been productionalized and has identified more than 250,000 fake accounts since deployment.
Starting Page	91
Ending Page	101
Page Count	11
File Format	PDF
ISBN	9781450338264
DOI	10.1145/2808769.2808779
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Fake profiles Classification Machine learning Clustering Data mining Spam detection
Content Type	Text
Resource Type	Article

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