Power Napping with Loud Neighbors: Optimal Energy-constrained Jamming and Anti-jamming. DeBruhl, B.; Kroer, C.; Datta, A.; Sandholm, T.; and Tague, P. In Proceedings of the ACM Conference on Security and Privacy in Wireless & Mobile Networks (WiSec), pages 117-128, 2014. ACM.
Power Napping with Loud Neighbors: Optimal Energy-constrained Jamming and Anti-jamming [link]Website  abstract   bibtex   
The openness of wireless communication and the recent development of software-defined radio technology, respectively, provide a low barrier and a wide range of capabilities for misbehavior, attacks, and defenses against attacks. In this work we present finite-energy jamming games, a game model that allows a jammer and sender to choose (1) whether to transmit or sleep, (2) a power level to transmit with, and (3) what channel to transmit on. We also allow the jammer to choose on how many channels it simultaneously attacks. A major addition in finite-energy jamming games is that the jammer and sender both have a limited amount of energy which is drained according to the actions a player takes. We develop a model of our system as a zero-sum finite-horizon stochastic game with deterministic transitions. We leverage the zero-sum and finite-horizon properties of our model to design a simple polynomial-time algorithm to compute optimal randomized strategies for both players. The utility function of our game model can be decoupled into a recursive equation. Our algorithm exploits this fact to use dynamic programming to construct solutions in a bottom-up fashion. For each state of energy levels, a linear program is solved to find Nash equilibrium strategies for the subgame. With these techniques, our algorithm has only a linear dependence on the number of states, and quadratic dependence on the number of actions, allowing us to solve very large instances. By computing Nash equilibria for our game models, we explore what kind of performance guarantees can be achieved both for the sender and jammer, when playing against an optimal opponent. We also use the optimal strategies to simulate finite-energy jamming games and provide insights into robust communication among reconfigurable, yet energy-limited, radio systems. To test the performance of the optimal strategies we compare their performance with a random and adaptive strategy. Matching our intuition, the aggressiveness of an attacker is related to how much of a discount is placed on data delay. This results in the defender often choosing to sleep despite the latency implication, because the threat of jamming is high. We also present several other findings from simulations where we vary the strategies for one or both of the players.
@inProceedings{
 title = {Power Napping with Loud Neighbors: Optimal Energy-constrained Jamming and Anti-jamming},
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 abstract = {The openness of wireless communication and the recent development of software-defined radio technology, respectively, provide a low barrier and a wide range of capabilities for misbehavior, attacks, and defenses against attacks. In this work we present finite-energy jamming games, a game model that allows a jammer and sender to choose (1) whether to transmit or sleep, (2) a power level to transmit with, and (3) what channel to transmit on. We also allow the jammer to choose on how many channels it simultaneously attacks. A major addition in finite-energy jamming games is that the jammer and sender both have a limited amount of energy which is drained according to the actions a player takes. We develop a model of our system as a zero-sum finite-horizon stochastic game with deterministic transitions. We leverage the zero-sum and finite-horizon properties of our model to design a simple polynomial-time algorithm to compute optimal randomized strategies for both players. The utility function of our game model can be decoupled into a recursive equation. Our algorithm exploits this fact to use dynamic programming to construct solutions in a bottom-up fashion. For each state of energy levels, a linear program is solved to find Nash equilibrium strategies for the subgame. With these techniques, our algorithm has only a linear dependence on the number of states, and quadratic dependence on the number of actions, allowing us to solve very large instances. By computing Nash equilibria for our game models, we explore what kind of performance guarantees can be achieved both for the sender and jammer, when playing against an optimal opponent. We also use the optimal strategies to simulate finite-energy jamming games and provide insights into robust communication among reconfigurable, yet energy-limited, radio systems. To test the performance of the optimal strategies we compare their performance with a random and adaptive strategy. Matching our intuition, the aggressiveness of an attacker is related to how much of a discount is placed on data delay. This results in the defender often choosing to sleep despite the latency implication, because the threat of jamming is high. We also present several other findings from simulations where we vary the strategies for one or both of the players.},
 bibtype = {inProceedings},
 author = {DeBruhl, Bruce and Kroer, Christian and Datta, Anupam and Sandholm, Tuomas and Tague, Patrick},
 booktitle = {Proceedings of the ACM Conference on Security and Privacy in Wireless & Mobile Networks (WiSec)}
}
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