Resilience of LTE eNode B against smart jammer in infinite-horizon asymmetric repeated zero-sum game

It has been proposed to use Long Term Evolution (LTE)/LTE-Advanced (LTE-A) networks for mission critical and public safety applications. However, LTE/LTE-A networks are known to be vulnerable to denial-of-service (DOS) and loss-of-service attacks from smart jammers. This article deals with the resilience of LTE/LTE-A eNode B against smart jamming attacks in an infinite-horizon asymmetric repeated zero-sum game and introduces algorithms for constructing efficient strategies for both players (smart jammer and eNode B) in such a game. It has been shown in game-theoretic literature that security strategies provide optimal solution in zero-sum games and that both players' security strategies in an infinite-horizon asymmetric repeated zero-sum game depend only on the history of informed player's actions. However, fixed-sized sufficient statistics are needed for both players to solve an infinite-horizon game efficiently with memory constraints. Smart jammer (informed player) uses its evolving belief state as the fixed-sized sufficient statistic for the repeated game. Whereas, LTE eNode B (uninformed player) uses worst-case regret of its security strategy and its anti-discounted update as the fixed-sized sufficient statistic. Although fixed-sized sufficient statistics are exploited by both players, optimal security strategy computation in lambda-discounted asymmetric games is still hard to compute because of non-convexity. Hence, the problem is convexified by devising suboptimal security strategies with guaranteed performance for both players that are based on approximated optimal game value. However, "approximated" strategies require full monitoring. Therefore, a simplistic yet effective "expected" strategy is also constructed for LTE eNode B (uninformed player) that does not require full monitoring. The simulation results show that smart jammer maintains its dominance at a long range of prior probability values by playing non-revealing and misleading strategies against the network for its long-term advantage. (C) 2020 Elsevier B.V. All rights reserved.