Adversarial machine learning and adversarial risk analysis in multi-source command and control

William N. Caballero; Mark Friend; Erik Blasch

doi:10.1117/12.2589027

12 April 2021 Adversarial machine learning and adversarial risk analysis in multi-source command and control

William N. Caballero, Mark Friend, Erik Blasch

Proceedings Volume 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX; 117560L (2021) https://doi.org/10.1117/12.2589027
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Over the last decade, various defense and security agencies have focused on methods of data and information fusion across multiple domains (e.g., space, air, land, sea, undersea, cyber, and information). Researchers and practitioners in these communities are currently emphasizing the importance of information warfare, algorithmic warfare, joint all domain operations, and multi-source availability. A related development extending from the data analytics community is adversarial machine learning (AML), i.e., the study of attacking and defending machine learning algorithms. It is generally the case in AML for a single algorithm to be considered. However, AML research regarding multi-source data manipulation is less developed because it compounds the challenges typically addressed. That is, attacks must be perceived over numerous information streams and their effects mitigated accordingly, often across multiple algorithms. This challenge is further complicated in multi-domain applications characterized by distributed control wherein agents have distinct capabilities (e.g., people or technological tools); the AML approaches required for operator infusion, information fusion, and control diffusion likely vary across each actor. Noting these challenges, this manuscript reviews command and control constructs, surveys related literature, and explores opportunities for adversarial risk analysis, a decision-theoretic alternative to game theory, to address AML in multi-source command and control settings.

Citation Download Citation

William N. Caballero, Mark Friend, and Erik Blasch "Adversarial machine learning and adversarial risk analysis in multi-source command and control", Proc. SPIE 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX, 117560L (12 April 2021); https://doi.org/10.1117/12.2589027

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available