Autonomous system identification and exploitation through observation of behavior

Lake Bookman; Dan Clymer; Jennifer Sierchio; Mark Gerken

doi:10.1117/12.2618929

6 June 2022 Autonomous system identification and exploitation through observation of behavior

Lake Bookman, Dan Clymer, Jennifer Sierchio, Mark Gerken

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121130B (2022) https://doi.org/10.1117/12.2618929
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Robust defenses to the new threats require early determination of the adversary’s plan of attack. At the same time, automated path-planning systems often behave predictably and produce paths with recognizable characteristic features. The increasing adoption of autonomous systems changes the defense landscape, thwarting traditional defenses with rapid decision speeds, but opening up new weaknesses. In this work, we investigate several possibilities to exploit artifacts of path planning algorithms that might be used as tells, giving a defending commander early warning and advantage in mounting a defense. With the application of an integrated air-defense system in mind, we examine a use case of several high-value targets which are obstructed by known defending threats. We use incoming time-series track data to predict the most probable targets and future trajectories of an enemy platform. One such approach is to attempt to directly learn the mapping from threat track to target. However, such an approach is likely brittle, requiring large volumes of data and substantial retraining for each target laydown. By contrast, we attempt to exploit predictable features from the path-planning algorithm itself, by first classifying the path-planning algorithm being used, and including that knowledge in our target prediction algorithm. We demonstrate that it is possible to differentiate classes of path planning algorithms with high accuracy based on track data alone. Utilizing the underlying model, we can then predict likely track updates and likely targets. We discuss strengths and limitations of this approach with respect to the aim of adding a robust tool to the air-defense use case.

Conference Presentation

Citation Download Citation

Lake Bookman, Dan Clymer, Jennifer Sierchio, and Mark Gerken "Autonomous system identification and exploitation through observation of behavior", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130B (6 June 2022); https://doi.org/10.1117/12.2618929

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