Cooperative monitoring for detecting adversarial communication

Taylor Sinatra; Cristina Comaniciu; Myron Hohil; Thomas LaPeruta

doi:10.1117/12.2588038

12 April 2021 Cooperative monitoring for detecting adversarial communication

Taylor Sinatra, Cristina Comaniciu, Myron Hohil, Thomas LaPeruta

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 1174612 (2021) https://doi.org/10.1117/12.2588038
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

In this paper, we propose an energy efficient cooperative monitoring algorithm for detecting and localizing adversarial communications in the presence of malicious reporting, Our solution is based on a combination of a game theory formulation for distributivity managing the monitoring-energy tradeoffs and a novel recursive trilateration algorithm which provides robustness to measurement errors and malicious reporting. Our results show that the energy consumption for monitoring is significantly reduced for our proposed solution compared to the all monitoring benchmark, and that the algorithm shows high robustness to malicious reporting. Our proposed method was able to still correctly localize the adversarial source within a specified tolerance error, and within a prescribed number of monitoring epochs, 83% of the time, when 25% of the monitoring nodes were compromised, reporting maliciously.

Conference Presentation

Citation Download Citation

Taylor Sinatra, Cristina Comaniciu, Myron Hohil, and Thomas LaPeruta "Cooperative monitoring for detecting adversarial communication", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 1174612 (12 April 2021); https://doi.org/10.1117/12.2588038

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