Secure data aggregation algorithms for sensor networks in the presence of collusion attacks

Anes Yessembayev, Dilip Sarkar

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

1 Scopus citations

Abstract

Aggregation of data from multiple sensor nodes is usually done by simple methods such as averaging or, more sophisticated, iterative filtering methods. However, such aggregation methods are highly vulnerable to malicious attacks where the attacker has knowledge of all sensed values and has ability to alter some of the readings. In this work, we develop and evaluate algorithms that eliminate or minimize the influence of altered readings. The basic idea is to consider altered data as outliers and find algorithms that effectively identify altered data as outliers and remove them. Once the outliers have been removed, use some standard technique to estimate a true value. Thus, the proposed data aggregation algorithm operates in two phases: removal of outliers and computation of an estimated true value from the remaining sensor data. Extensive evaluation of the proposed algorithms shows that they significantly outperform all existing methods.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509019410
DOIs
StatePublished - Apr 19 2016
Event13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016 - Sydney, Australia
Duration: Mar 14 2016Mar 18 2016

Other

Other13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
CountryAustralia
CitySydney
Period3/14/163/18/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Human-Computer Interaction

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    Yessembayev, A., & Sarkar, D. (2016). Secure data aggregation algorithms for sensor networks in the presence of collusion attacks. In 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016 [7457155] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PERCOMW.2016.7457155