Improving robustness of a popular probabilistic clustering algorithm against insider attacks

Sayed M. Sayed, Tom La Porta, Simone Silvestri, Patrick McDaniel

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

Abstract

Many clustering algorithms for mesh, ad hoc and Wireless Sensor Networks have been proposed. Probabilistic approaches are a popular class of such algorithms. However, it is essential to analyze their robustness against security compromise. We study the robustness of EEHCA, a popular energy efficient clustering algorithm as an example of probabilistic class in terms of security compromise. In this paper, we investigate attacks on EEHCA through analysis and experimental simulations. We analytically characterize two different attack models. In the first attack model, the attacker aims to gain control over the network by stealing network traffic, or by disrupting the data aggregation process (integrity attack). In the second attack model, the inducement of the attacker is to abridge the network lifetime (denial of service attack). We assume the clustering algorithm is running periodically and propose a detection solution by exploiting Bernoulli CUSUM charts.

Original languageEnglish
Title of host publicationSecurity and Privacy in Communication Networks - 16th EAI International Conference, SecureComm 2020, Proceedings
EditorsNoseong Park, Kun Sun, Sara Foresti, Kevin Butler, Nitesh Saxena
Pages381-401
Number of pages21
DOIs
StatePublished - 2020
Event16th International Conference on Security and Privacy in Communication Networks, SecureComm 2020 - Washington, United States
Duration: Oct 21 2020Oct 23 2020

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume335
ISSN (Print)1867-8211

Conference

Conference16th International Conference on Security and Privacy in Communication Networks, SecureComm 2020
Country/TerritoryUnited States
CityWashington
Period10/21/2010/23/20

Bibliographical note

Funding Information:
Acknowledgments. Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-13-2-0045 (ARL Cyber Security CRA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.

Keywords

  • Anomaly detection
  • CUSUM test
  • Probabilistic clustering algorithm

ASJC Scopus subject areas

  • Computer Networks and Communications

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