Journal
CCS'14: PROCEEDINGS OF THE 21ST ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY
Volume -, Issue -, Pages 477-488Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2660267.2660269
Keywords
Malicious account detection; scalable clustering system; online social networks
Categories
Funding
- NSF [CNS-0845858, CNS-1017858]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1017858] Funding Source: National Science Foundation
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The success of online social networks has attracted a constant interest in attacking and exploiting them. Attackers usually control malicious accounts, including both fake and compromised real user accounts, to launch attack campaigns such as social spam, malware distribution, and online rating distortion. To defend against these attacks, we design and implement a malicious account detection system called SynchroTrap. We observe that malicious accounts usually perform loosely synchronized actions in a variety of social network context. Our system clusters user accounts according to the similarity of their actions and uncovers large groups of malicious accounts that act similarly at around the same time for a sustained period of time. We implement SynchroTrap as an incremental processing system on Hadoop and Gi-raph so that it can process the massive user activity data in a large online social network efficiently. We have deployed our system in five applications at Facebook and Instagram. SynchroTrap was able to unveil more than two million malicious accounts and 1156 large attack campaigns within one month.
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