Pollution attack: A new attack against localization in wireless sensor networks

Yingpei Zeng, Jiannong Cao, Shigeng Zhang, Shanqing Guo, Li Xie

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Many secure localization algorithms have been proposed. In these algorithms, collusion attack is usually considered as the strongest attack when evaluating their performance. Also, for ensuring correct localization under the collusion attack, a necessary number of normal beacons are needed and a lower bound on this number has been established (assuming the errors of distance measurements are ignorable). In this paper, we introduce pollution attack, a more powerful attack which can succeed even when the number of normal beacons is more than the lower bound. In this attack, victim node is misled to a special chosen location, which results in a confusion of compromised beacon with normal beacon. We propose a new metric to measure the vulnerability of a normal location reference set to pollution attack, and develop two algorithms to efficiently compute the value of the proposed metric. We also present a method to judge whether the output of the localization algorithm is credible under pollution attack. Simulation results show that the pollution attack can succeed with high probability.
Original languageEnglish
Title of host publication2009 IEEE Wireless Communications and Networking Conference, WCNC 2009 - Proceedings
DOIs
Publication statusPublished - 22 Sept 2009
Event2009 IEEE Wireless Communications and Networking Conference, WCNC 2009 - Budapest, Hungary
Duration: 5 Apr 20098 Apr 2009

Conference

Conference2009 IEEE Wireless Communications and Networking Conference, WCNC 2009
Country/TerritoryHungary
CityBudapest
Period5/04/098/04/09

ASJC Scopus subject areas

  • General Engineering

Fingerprint

Dive into the research topics of 'Pollution attack: A new attack against localization in wireless sensor networks'. Together they form a unique fingerprint.

Cite this