Resilience of LTE networks against smart jamming attacks: A game-theoretic approach

LTE/LTE-advanced;Smart jamming;Repeated game with asymmetric information;Threat mechanism;Game-theoretic learning;Regret minimization;Algorithms

Aziz, Farhan Muhammad ; Shamma, Jeff S. Stuber, Gordon L. Electrical and Computer Engineering McLaughlin, Steven W. Feron, Eric M. J. Bloch, Matthieu R. Romberg, Justin K. ; Shamma, Jeff S.

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: LTE/LTE-advanced;    Smart jamming;    Repeated game with asymmetric information;    Threat mechanism;    Game-theoretic learning;    Regret minimization;    Algorithms;   

Others  :  https://smartech.gatech.edu/bitstream/1853/58635/1/AZIZ-DISSERTATION-2017.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The objectives of this research are to identify security vulnerabilities in LTE/LTE-A air interface; model the network and the smart jammer dynamics under realistic constraints; and devise adept algorithms that can help the network combat smart jamming attacks autonomously. LTE/LTE-A networks provide advanced data, Voice-over-IP (VoIP), multimedia, and location-based services to more than a billion subscribers around the world. Lately, it has been suggested to utilize commercially and privately-owned LTE/LTE-A networks for mission-critical applications like public safety, smart grid and military communications. Although LTE/LTE-A air interface provides ease of accessibility, flexibility, mobility support, low latency, high data rates, and economy of scale, it also raises serious security concerns. It is shown that the LTE air interface is vulnerable to denial-of-service (DoS) and loss of service attacks from power and bandwidth-limited smart jammers, without being hacked by them. The interaction between the network and the smart jammer is modeled as a two-player infinite-horizon Bayesian game with asymmetric information, with the network being the uninformed player. This research investigates the smart jamming problem in LTE/LTE-A networks, by using heuristic analysis, threat mechanism, game-theoretic and reinforcement learning and regret minimization in repeated games to construct autonomous policies for the network to help it combat these attacks. Moreover, this work is focused on devising policies (algorithms) that can be practically deployed in current networks under realistic constraints, without modifying 3GPP specifications.