Detecting Clusters of Fake Accounts in Online Social Networks

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 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 uni...

• Type: Journal
• Year: 2015
• Where: CCS
• Authors: Cao Xiao, David Mandell Freeman, Theodore Hwa
• Keywords: CCS 2015, Security Privacy