Abstract
In this work we consider the problem of defending against adversarial attacks from UAV swarms performing complex maneuvers, driven by multiple, dynamically changing, leaders. We rely on short-time observations of the trajectories of the UAVs and develop a leader detection scheme based on the notion of Granger causality. We proceed with the estimation of the swarm’s coordination laws, modeled by a generalized Cucker-Smale model with non-local repulsive potential functions and dynamically changing leaders, through an appropriately defined iterative optimization algorithm. Similar problems exist in communication and computer networks, as well as social networks over the Internet. Thus, the methodology and algorithms proposed can be applied to many types of network swarms including detection of influential malevolent “sources” of attacks and “miss-information”. The proposed algorithms are robust to missing data and noise. We validate our methodology using simulation data of complex swarm movements.
This work was partially supported by the Defense Advanced Research Projects Agency (DARPA) under Agreement No. HR00111990027, by ONR grant N00014-17-1-2622, and by a grant from Northrop Grumman Corporation.
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Mavridis, C.N., Suriyarachchi, N., Baras, J.S. (2020). Detection of Dynamically Changing Leaders in Complex Swarms from Observed Dynamic Data. In: Zhu, Q., Baras, J.S., Poovendran, R., Chen, J. (eds) Decision and Game Theory for Security. GameSec 2020. Lecture Notes in Computer Science(), vol 12513. Springer, Cham. https://doi.org/10.1007/978-3-030-64793-3_12
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