Experimental Evaluation of Stealthy Attack Detection in a Robot

G. Sabaliauskaite, G.S. Ng, J. Ruths, A. Mathur

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

6 Citations (Scopus)

Abstract

An experiment was conducted to investigate the effectiveness of the Cumulative Sum (CUSUM) approach for detecting cyber attacks on Cyber Physical Systems (CPS). The Amigobot robot was used as the CPS in this study. Three types of stealthy attacks were considered, namely, surge, bias, and geometric. While a similar study has been reported earlier using a simulated chemical plant, the objective of the study reported here was to replicate the previous study in a realistic CPS environment and investigate whether the detection method performs differently. Cyber attacks were implemented on the Amigobot through its wireless control mechanism by changing the readings obtained from one of its sonar sensors. In addition, the investigation focused on understanding the impact of attack timing and duration on (a) detection effectiveness of the CUSUM method and (b) system safety. Analysis of experimental data indicates differences between results reported in the previous simulation-based study and those reported here.
Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 21st Pacific Rim International Symposium on Dependable Computing, PRDC 2015
PublisherIEEE
ISBN (Electronic) 978-1-4673-9376-8
DOIs
Publication statusPublished - 7 Jan 2016
Externally publishedYes
Event21st Pacific Rim International Symposium on Dependable Computing (PRDC) - Zhangjiajie, China
Duration: 18 Nov 201520 Nov 2015

Conference

Conference21st Pacific Rim International Symposium on Dependable Computing (PRDC)
CountryChina
CityZhangjiajie
Period18/11/1520/11/15

Keywords

  • Cyber security
  • Cyber Physical Systems
  • Cyber attacks
  • Bias
  • and geometric attacks
  • CUSUM method

Fingerprint Dive into the research topics of 'Experimental Evaluation of Stealthy Attack Detection in a Robot'. Together they form a unique fingerprint.

Cite this