Sensor Scheduling in Intrusion Detection Games with Uncertain Payoffs

Jayanth Bhargav, Shreyas Sundaram, Mahsa Ghasemi
Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, PMLR 283:606-618, 2025.

Abstract

We study the problem of sensor scheduling for an intrusion detection task. We model this as a 2-player zero-sum game over a graph, where the defender (Player 1) seeks to identify the optimal strategy for scheduling sensor orientations to minimize the probability of missed detection at minimal cost, while the intruder (Player 2) aims to identify the optimal path selection strategy to maximize missed detection probability at minimal cost. The defender’s strategy space grows exponentially with the number of sensors, making direct computation of the Nash Equilibrium (NE) strategies computationally expensive. To tackle this, we propose a distributed variant of the Weighted Majority algorithm that exploits the structure of the game’s payoff matrix, enabling efficient computation of the NE strategies with provable convergence guarantees. Next, we consider a more challenging scenario where the defender lacks knowledge of the true sensor models and, consequently, the game’s payoff matrix. For this setting, we develop online learning algorithms that leverage bandit feedback from sensors to estimate the NE strategies. By building on existing results from perturbation theory and online learning in matrix games, we derive high-probability order-optimal regret bounds for our algorithms. Finally, through simulations, we demonstrate the empirical performance of our proposed algorithms in both known and unknown payoff scenarios.

Cite this Paper

BibTeX
@InProceedings{pmlr-v283-bhargav25a, title = {Sensor Scheduling in Intrusion Detection Games with Uncertain Payoffs}, author = {Bhargav, Jayanth and Sundaram, Shreyas and Ghasemi, Mahsa}, booktitle = {Proceedings of the 7th Annual Learning for Dynamics \& Control Conference}, pages = {606--618}, year = {2025}, editor = {Ozay, Necmiye and Balzano, Laura and Panagou, Dimitra and Abate, Alessandro}, volume = {283}, series = {Proceedings of Machine Learning Research}, month = {04--06 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v283/main/assets/bhargav25a/bhargav25a.pdf}, url = {https://proceedings.mlr.press/v283/bhargav25a.html}, abstract = {We study the problem of sensor scheduling for an intrusion detection task. We model this as a 2-player zero-sum game over a graph, where the defender (Player 1) seeks to identify the optimal strategy for scheduling sensor orientations to minimize the probability of missed detection at minimal cost, while the intruder (Player 2) aims to identify the optimal path selection strategy to maximize missed detection probability at minimal cost. The defender’s strategy space grows exponentially with the number of sensors, making direct computation of the Nash Equilibrium (NE) strategies computationally expensive. To tackle this, we propose a distributed variant of the Weighted Majority algorithm that exploits the structure of the game’s payoff matrix, enabling efficient computation of the NE strategies with provable convergence guarantees. Next, we consider a more challenging scenario where the defender lacks knowledge of the true sensor models and, consequently, the game’s payoff matrix. For this setting, we develop online learning algorithms that leverage bandit feedback from sensors to estimate the NE strategies. By building on existing results from perturbation theory and online learning in matrix games, we derive high-probability order-optimal regret bounds for our algorithms. Finally, through simulations, we demonstrate the empirical performance of our proposed algorithms in both known and unknown payoff scenarios.} }
Endnote
%0 Conference Paper %T Sensor Scheduling in Intrusion Detection Games with Uncertain Payoffs %A Jayanth Bhargav %A Shreyas Sundaram %A Mahsa Ghasemi %B Proceedings of the 7th Annual Learning for Dynamics \& Control Conference %C Proceedings of Machine Learning Research %D 2025 %E Necmiye Ozay %E Laura Balzano %E Dimitra Panagou %E Alessandro Abate %F pmlr-v283-bhargav25a %I PMLR %P 606--618 %U https://proceedings.mlr.press/v283/bhargav25a.html %V 283 %X We study the problem of sensor scheduling for an intrusion detection task. We model this as a 2-player zero-sum game over a graph, where the defender (Player 1) seeks to identify the optimal strategy for scheduling sensor orientations to minimize the probability of missed detection at minimal cost, while the intruder (Player 2) aims to identify the optimal path selection strategy to maximize missed detection probability at minimal cost. The defender’s strategy space grows exponentially with the number of sensors, making direct computation of the Nash Equilibrium (NE) strategies computationally expensive. To tackle this, we propose a distributed variant of the Weighted Majority algorithm that exploits the structure of the game’s payoff matrix, enabling efficient computation of the NE strategies with provable convergence guarantees. Next, we consider a more challenging scenario where the defender lacks knowledge of the true sensor models and, consequently, the game’s payoff matrix. For this setting, we develop online learning algorithms that leverage bandit feedback from sensors to estimate the NE strategies. By building on existing results from perturbation theory and online learning in matrix games, we derive high-probability order-optimal regret bounds for our algorithms. Finally, through simulations, we demonstrate the empirical performance of our proposed algorithms in both known and unknown payoff scenarios.
APA
Bhargav, J., Sundaram, S. & Ghasemi, M.. (2025). Sensor Scheduling in Intrusion Detection Games with Uncertain Payoffs. Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, in Proceedings of Machine Learning Research 283:606-618 Available from https://proceedings.mlr.press/v283/bhargav25a.html.

Related Material