Adversarial attacks and countermeasures against ML models in army multi-domain operations

Onur Savas; Lei Ding; Teresa Papaleo; Ian McCulloh

doi:10.1117/12.2548798

19 May 2020 Adversarial attacks and countermeasures against ML models in army multi-domain operations

Onur Savas, Lei Ding, Teresa Papaleo, Ian McCulloh

Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 114130S (2020) https://doi.org/10.1117/12.2548798
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

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.

Citation Download Citation

Onur Savas, Lei Ding, Teresa Papaleo, and Ian McCulloh "Adversarial attacks and countermeasures against ML models in army multi-domain operations", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114130S (19 May 2020); https://doi.org/10.1117/12.2548798

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