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Proceedings Paper

Security engineering with machine learning for adversarial resiliency in cyber physical systems
Author(s): Felix O. Olowononi; Danda B. Rawat; Moses Garuba; Charles Kamhoua
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Paper Abstract

Recent technological advances provide the opportunities to bridge the physical world with cyber-space that leads to complex and multi-domain cyber physical systems (CPS) where physical systems are monitored and controlled using numerous smart sensors and cyber space to respond in real-time based on their operating environment. However, the rapid adoption of smart, adaptive and remotely accessible connected devices in CPS makes the cyberspace more complex and diverse as well as more vulnerable to multitude of cyber-attacks and adversaries. In this paper, we aim to design, develop and evaluate a distributed machine learning algorithm for adversarial resiliency where developed algorithm is expected to provide security in adversarial environment for critical mobile CPS.

Paper Details

Date Published: 10 May 2019
PDF: 7 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061O (10 May 2019); doi: 10.1117/12.2519372
Show Author Affiliations
Felix O. Olowononi, Howard Univ. (United States)
Danda B. Rawat, Howard Univ. (United States)
Moses Garuba, Howard Univ. (United States)
Charles Kamhoua, U.S. Army Research Lab. (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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