Security engineering with machine learning for adversarial resiliency in cyber physical systems

Felix O. Olowononi; Danda B. Rawat; Moses Garuba; Charles Kamhoua

doi:10.1117/12.2519372

10 May 2019 Security engineering with machine learning for adversarial resiliency in cyber physical systems

Felix O. Olowononi, Danda B. Rawat, Moses Garuba, Charles Kamhoua

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 110061O (2019) https://doi.org/10.1117/12.2519372
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Felix O. Olowononi, Danda B. Rawat, Moses Garuba, and Charles Kamhoua "Security engineering with machine learning for adversarial resiliency in cyber physical systems", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061O (10 May 2019); https://doi.org/10.1117/12.2519372

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