Behavior and Vulnerability Assessment of Drones-Enabled Industrial Internet of Things (IIoT)

Vishal Sharma, Gaurav Choudhary, Yongho Ko, Ilsun You*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
1 Downloads (Pure)

Abstract

Accessibility to industrial processes and direct obtaining of the desired services are the major facilities of Industrial Internet of Things (IIoT). IIoT covers crucial aspects of smart systems, such as automation, keenly intellective setups, asset management, and user-industry collaboration. These user-industry setups are facilitated by modern era network technologies, which also include an immense dependence on drones as one of the on-demand components for amending the quality and maximizing the coverage. However, these kinds of network formations require precise operations of drones and their perpetual assessment. The existing studies have highlighted these issues but fail to provide the behavior as well as the vulnerability evaluations of drones enabled IIoT. In addition, the existing studies are unable to provide statewise verification of drones and do not recognize anomaly drones based on their behavior over varying properties. Furthermore, the existing solutions lack facilities for including security policies which help in assessing the vulnerabilities with a higher accuracy. This paper fills this gap by using a novel N-layered hierarchical context-aware aspect-oriented Petri net model that not only evaluates the drone behavior but also assesses it for potential vulnerabilities by the utilization of security policies. Statewise verification is performed for the proposed model along with a simulation study, which designates its paramountcy in providing low-complex and low-overhead-based solution with a detection rate higher than 95% and accuracy as high as 99.9%. The proposed approach increases the probability of selecting a correct drone by 81.71% even in the case of a high number of failures.

Original languageEnglish
Article number8411430
Pages (from-to)43368-43383
Number of pages16
JournalIEEE Access
Volume6
DOIs
Publication statusPublished - 16 Jul 2018
Externally publishedYes

Keywords

  • Behavior modeling
  • drones
  • HCAPN
  • IIoT
  • vulnerability assessment

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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