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Proceedings Paper

Adversarial attacks and countermeasures against ML models in army multi-domain operations
Author(s): Onur Savas; Lei Ding; Teresa Papaleo; Ian McCulloh
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Paper Abstract

To systematically understand the effects of vulnerabilities introduced by AI/ML-enabled Army Multi-domain Operations, we provide an overview of characterization of ML attacks with an emphasis on black-box vs. white-box attacks. We then study a system and attack model for Army MDO applications and services, and introduce the roles of stakeholders in this system. We show, in various attack scenarios and under different knowledges of the deployed system, how peer adversaries can employ deceptive techniques to defeat algorithms, and how the system should be designed to minimize the attacks. We demonstrate the feasibility of our approach in a cyber threat intelligence use case. We conclude with a path forward for design and policy recommendations for robust and secure deployment of AI/ML applications in Army MDO environments.

Paper Details

Date Published: 19 May 2020
PDF: 6 pages
Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114130S (19 May 2020); doi: 10.1117/12.2548798
Show Author Affiliations
Onur Savas, Accenture (United States)
Lei Ding, Accenture (United States)
Teresa Papaleo, Accenture (United States)
Ian McCulloh, Accenture (United States)


Published in SPIE Proceedings Vol. 11413:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II
Tien Pham; Latasha Solomon; Katie Rainey, Editor(s)

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