A Sensitivity Analysis of Evolutionary Algorithms in Generating Secure Configurations

Shuvalaxmi Dass, Akbar Siami Namin

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

Abstract

The growth of Cyber-physical Systems (CPS) has been increased in recent years. This has led to the coupling of highly complex cyber-physical components. With the integration of such complex components, new security challenges have emerged. Studies involving security issues in CPS have been quite difficult to be generalized due to the presence of heterogeneity and the diversity of the CPS components. These systems are subject to various vulnerabilities, threats and attacks, as a consequence of complex versions of CPS being introduced over time. This paper deals with vulnerabilities caused due to improper configurations in the software component of cyber-physical systems. Evolutionary algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) can be employed to adequately test the underlying software for certain categories of vulnerabilities. This paper provides a detailed sensitivity analysis of these evolutionary algorithms in order to find out whether changing parameters involved in tuning these algorithms affect the overall performance. This analysis is based on the estimate of the number of generation of secure vulnerability pattern vectors under the variation of different parameters. The results indicate that while there is no evidence of influential parameters in Genetic Algorithms (i.e., mutation rate and population size), changes in the parameters involved in Particle Swarm Optimization algorithms (i.e., velocity rate and fitness range) have some positive impacts on the number of secure configurations generated.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2065-2072
Number of pages8
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

Keywords

  • Cyber-Physical Systems
  • Genetic Algorithm
  • Particle Swarm Optimization
  • Sensitivity Analysis
  • security

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