SocialWatch: Detection of Online Service Abuse via Large-Scale Social Graphs

Junxian Huang, Yinglian Xie, Fang Yu, Qifa Ke, Martin Abadi, Eliot Gillum, and Z. Morley Mao

Abstract

attacker-created accounts and hijacked accounts for online services

at a large scale. SocialWatch explores a set of social graph properties

that effectively model the overall social activity and connectivity

patterns of online users, including degree, PageRank, and

social affinity features. These features are hard to mimic and robust

to attacker counter strategies. We evaluate SocialWatch using

a large, real dataset with more than 682 million users and over 5.75

billion directional relationships. SocialWatch successfully detects

56.85 million attacker-created accounts with a low false detection

rate of 0.75% and a low false negative rate of 0.61%. In addition,

SocialWatch detects 1.95 million hijacked accounts—among

which 1.23 million were not detected previously—with a low false

detection rate of 2%. Our work demonstrates the practicality and

effectiveness of using large social graphs with billions of edges to

detect real attacks.

Details

Publication typeInproceedings
Published in8th ACM Symposium on Information, Computer and Communications Security (AsiaCCS), to appear
PublisherACM
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