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

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.