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AbstractLTE/LTE-Advanced networks are known to be vulnerable to denial-of-service (DOS) and loss-of-service attacks from smart jammers. The interaction between the network and the smart jammer has been modeled as an infinite-horizon general-sum (non-zero-sum) Bayesian game with asymmetric information, with the network being the uninformed player. Although significant work has been done on optimal strategy computation and control of information revelation of the informed player in repeated asymmetric information games, it has been limited to zero-sum games with perfect monitoring. Recent progress on the strategy computation of the uninformed player is also limited to zero-sum games with perfect monitoring and is focused on expected payoff formulations. Since the proposed formulation is a general-sum game with imperfect monitoring, existing formulations cannot be leveraged for estimating true state of nature (the jammer type). Hence, a threat-based mechanism is proposed for the uninformed player (the network) to estimate the informed player’s type (jammer type). The proposed mechanism helps the network resolve uncertainty about the state of nature (jammer type) so that it can compute a repeated-game strategy conditioned on its estimate. The proposed algorithm does not rely on the commonly assumed “full monitoring” premise, and uses a combination of threat-based mechanism and non-parametric estimation to estimate the jammer type. In addition, it does not require any explicit feedback from the network users nor does it rely on a specific distribution (e.g., Gaussian) of test statistic. It is shown that the proposed algorithm’s estimation performance is quite robust under realistic modeling and observational constraints despite all the aforementioned challenges.
CitationAziz F, Shamma J, Stuber GL (2017) Jammer Type Estimation in LTE with a Smart Jammer Repeated Game. IEEE Transactions on Vehicular Technology: 1–1. Available: http://dx.doi.org/10.1109/tvt.2017.2672682.
SponsorsThe research reported in this publication was supported in part by funding from the US AFOSR/MURI project # FA9550-10-1-0573, the US ARO project # W911NF-09-1-0553, and the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.