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Proceedings Paper

On the machine learning for minimizing the negative influence in mobile cyber physical systems
Author(s): Vijay Chaudhary; Danda B. Rawat
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Paper Abstract

Emerging cyber physical system (CPS) are expected to enhance the overall performance of the networked systems to provide reliable services and applications to their users. However, massive number of connectivities in CPS bring security vulnerabilities and the mobility adds more complexity for securing the mobile CPS. Any mobile CPS can be represented as a graph with connectivity as well as with interactions among a group of mobile CPS nodes that plays a major role as a medium for the propagation of wrong/right information, and influence its members in the mobile CPS. This problem has wide spread applications in viral information disseminating in mobile CPS, where a malicious mobile CPS node may wish to spread the rumor via the most influential individuals in mobile CPS. In this paper, we design, develop and evaluate a machine learning approach that is based on a set theoretic approach for optimizing the influence in mobile CPS. This problem has applications in civilian and military systems.

Paper Details

Date Published: 10 May 2019
PDF: 8 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061P (10 May 2019); doi: 10.1117/12.2519598
Show Author Affiliations
Vijay Chaudhary, Howard Univ. (United States)
Danda B. Rawat, Howard Univ. (United States)


Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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