Insider attacker detection in wireless sensor networks

Fang Liu*, Xiuzhen Cheng, Dechang Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

187 Scopus citations


Though destructive to network functions, insider attackers are not detectable with only the classic cryptographybased techniques. Many mission-critic sensor network applications demand an effective, light, flexible algorithm for internal adversary identification with only localized information available. The insider attacker detection scheme proposed in this paper meets all the requirements by exploring the spatial correlation existent among the networking behaviors of sensors in close proximity. Our work is exploratory in that the proposed algorithm considers multiple attributes simultaneously in node behavior evaluation, with no requirement on a prior knowledge about normal/malicious sensor activities. Moreover, it is applicationfriendly, which employs original measurements from sensors and can be employed to monitor many aspects of sensor networking behaviors. Our algorithm is purely localized, fitting well to the large-scale sensor networks. Simulation results indicate that internal adversaries can be identified with a high accuracy and a low false alarm rate when as many as 25% sensors are misbehaving.

Original languageEnglish
Title of host publicationProceedings - IEEE INFOCOM 2007
Subtitle of host publication26th IEEE International Conference on Computer Communications
Number of pages9
StatePublished - 2007
Externally publishedYes
EventIEEE INFOCOM 2007: 26th IEEE International Conference on Computer Communications - Anchorage, AK, United States
Duration: 6 May 200712 May 2007

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


ConferenceIEEE INFOCOM 2007: 26th IEEE International Conference on Computer Communications
Country/TerritoryUnited States
CityAnchorage, AK


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