AI-Enabled Jammer Deception Using Decoy Packets
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S. Frisbie and M. Younis, "AI-Enabled Jammer Deception Using Decoy Packets," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 5013-5018, doi: 10.1109/GLOBECOM48099.2022.10001651.
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Abstract
In this work, we present a learning algorithm for a
wireless communications network to transmit decoy packets to
counter an adversarial sensing-reactive jammer. As the jammer
is required to search across channels for data transmissions,
decoy packets can have the effect of stalling the jammer on a
particular channel, preventing it from continuing its search and
leaving legitimate packets unimpeded. A reinforcement learning
algorithm trains a deep neural network with an explorationexploitation algorithm and experience replay. The state- and
action-space and reward function are presented as components
of the reinforcement learning framework. Our algorithm is tested
with software simulations, modeling ZigBee communications
nodes using time-division multiple access for medium access
control. A reactive jammer is modeled in the simulation, with the
goal of disrupting any detected ZigBee transmissions. A means
to measure and distribute the reward function and system state
to enable edge-learning in this context is presented as part of
the implementation. The results demonstrate the effectiveness of
our algorithm in mitigating the jamming attack, outperforming
a random decoy strategy by a factor of two.