Command and control with poisoned temporal batch data

Tahir Ekin; Vamshi Garega

doi:10.1117/12.2663283

12 June 2023 Command and control with poisoned temporal batch data

Tahir Ekin, Vamshi Garega

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125380G (2023) https://doi.org/10.1117/12.2663283
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Data manipulation could alter the performance of joint all domain command and-control decisions (JADC2). We present a Bayesian decision theoretic approach for adversarial forecasting when the underlying data collected over time is subject to attack from intelligent adversaries. Proposed adversarial risk analysis-based framework allows incomplete information and uncertainty. We solve the adversary’s poisoning decision problem where he manipulates batch data being inputted into the forecasting method of statistical autoregressive models. The findings show the vulnerability of forecasting models under adversarial activities. We discuss potential defender strategies.

Conference Presentation

Citation Download Citation

Tahir Ekin and Vamshi Garega "Command and control with poisoned temporal batch data", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125380G (12 June 2023); https://doi.org/10.1117/12.2663283

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