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Activity Number: 27 - SDNS Speed Session
Type: Contributed
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Defense and National Security
Abstract #318818
Title: Adversarial Forecasting for Command and Control Decisions
Author(s): Tahir Ekin* and William N. Caballero and David Rios Insua
Companies: Texas State University and Air Force Institute of Technology and ICMAT-CSIC
Keywords: machine learning; adversarial risk analysis; adversarial machine learning; adversarial forecasting; military decision making; command and control decisions
Abstract:

All command and control (C2) decisions are founded upon information. Direct manipulation of this information can significantly alter a C2 system’s performance, thereby impacting the quality of the resulting decisions. One such example relates to the targeting of forecasting algorithms that predict operational conditions. Although the forecasting model provides legitimate output, the underlying data may be corrupted by a data-fiddler. To address such challenges, we propose an adversarial hidden Markov Model that maximizes expected performance under adversarial risk analysis assumptions.


Authors who are presenting talks have a * after their name.

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