Toward worm detection in online social networks

Wei Xu, Fangfang Zhang, Sencun Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

67 Scopus citations


Worms propagating in online social networking (OSN) websites have become a major security threat to both the websites and their users in recent years. Since these worms exhibit unique propagation vectors, existing Internet worm detection mechanisms cannot be applied to them. In this work, we propose an early warning OSN worms detection system, which leverages both the propagation characteristics of these worms and the topological properties of online social networks. Our system can effectively monitor the entire social graph by keeping only a small number of user accounts under surveillance. Moreover, the system applies a two-level correlation scheme to reduce the noise from normal user communications such that infected user accounts can be identified with a higher accuracy. Our evaluation on the real social graph data obtained from Flickr indicates that by monitoring five hundreds users out of 1.8 million users, the proposed detection system can detect the burst of an OSN worm when less than 0.13% of total user accounts are infected. Besides, by adopting simple countermeasures, the detection system is also shown to be very helpful for worm containment.

Original languageEnglish (US)
Title of host publicationProceedings - 26th Annual Computer Security Applications Conference, ACSAC 2010
PublisherIEEE Computer Society
Number of pages10
ISBN (Print)9781450301336
StatePublished - 2010

Publication series

NameProceedings - Annual Computer Security Applications Conference, ACSAC
ISSN (Print)1063-9527

All Science Journal Classification (ASJC) codes

  • Software
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


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