Power napping with loud neighbors: optimal energy-constrained jamming and anti-jamming

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.

[1]  Tamer Basar,et al.  A dynamic transmitter-jammer game with asymmetric information , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[2]  Zhu Han,et al.  Physical layer security game: How to date a girl with her boyfriend on the same table , 2009, 2009 International Conference on Game Theory for Networks.

[3]  Anthony Ephremides,et al.  MAC games for distributed wireless network security with incomplete information of selfish and malicious user types , 2009, 2009 International Conference on Game Theory for Networks.

[4]  Quanyan Zhu,et al.  Game theory meets network security and privacy , 2013, CSUR.

[5]  Ranjan K. Mallik,et al.  Analysis of an on-off jamming situation as a dynamic game , 2000, IEEE Trans. Commun..

[6]  Jean-Pierre Hubaux,et al.  Game Theory in Wireless Networks: A Tutorial , 2006 .

[7]  Tuomas Sandholm,et al.  Computing Equilibria in Multiplayer Stochastic Games of Imperfect Information , 2009, IJCAI.

[8]  Loukas Lazos,et al.  Selective jamming/dropping insider attacks in wireless mesh networks , 2011, IEEE Network.

[9]  Xin Liu,et al.  Broadcast Control Channel Jamming: Resilience and Identification of Traitors , 2007, 2007 IEEE International Symposium on Information Theory.

[10]  Zachary Weinberg,et al.  STIR-ing the wireless medium with self-tuned, inference-based, real-time jamming , 2012, 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012).

[11]  Xiaofeng Wang,et al.  Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games , 2002, NIPS.

[12]  Patrick Tague,et al.  Keeping up with the jammers: Observe-and-adapt algorithms for studying mutually adaptive opponents , 2014, Pervasive Mob. Comput..

[13]  Ivan Martinovic,et al.  Short paper: reactive jamming in wireless networks: how realistic is the threat? , 2011, WiSec '11.

[14]  Michael P. Wellman,et al.  Nash Q-Learning for General-Sum Stochastic Games , 2003, J. Mach. Learn. Res..

[15]  Walid Saad,et al.  Eavesdropping and jamming in next-generation wireless networks: A game-theoretic approach , 2011, 2011 - MILCOM 2011 Military Communications Conference.

[16]  Michael L. Littman,et al.  Friend-or-Foe Q-learning in General-Sum Games , 2001, ICML.

[17]  Christian Scheideler,et al.  A Jamming-Resistant MAC Protocol for Multi-Hop Wireless Networks , 2010, DISC.

[18]  Wenyuan Xu,et al.  Jamming sensor networks: attack and defense strategies , 2006, IEEE Network.

[19]  Radha Poovendran,et al.  Jamming-Aware Traffic Allocation for Multiple-Path Routing Using Portfolio Selection , 2011, IEEE/ACM Transactions on Networking.

[20]  Eitan Altman,et al.  Zero-sum constrained stochastic games with independent state processes , 2005, Math. Methods Oper. Res..

[21]  Tamer Basar,et al.  Switching behavior in optimal communication strategies for team jamming games under resource constraints , 2011, 2011 IEEE International Conference on Control Applications (CCA).

[22]  Eitan Altman,et al.  Transmission Power Control Game with SINR as Objective Function , 2008, NET-COOP.

[23]  Yishay Mansour,et al.  Fast Planning in Stochastic Games , 2000, UAI.

[24]  Radha Poovendran,et al.  Optimal Jamming Attacks and Network Defense Policies in Wireless Sensor Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[25]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[26]  Koorosh Firouzbakht,et al.  On the capacity of rate-adaptive packetized wireless communication links under jamming , 2012, WISEC '12.

[27]  H. T. Kung,et al.  Competing Mobile Network Game: Embracing antijamming and jamming strategies with reinforcement learning , 2013, 2013 IEEE Conference on Communications and Network Security (CNS).

[28]  Xin Liu,et al.  SPREAD: Foiling Smart Jammers Using Multi-Layer Agility , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[29]  J. D. Lohn,et al.  An in-situ optimized anti-jamming beamformer for mobile signals , 2012, Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation.

[30]  Maxim Raya,et al.  DOMINO: Detecting MAC Layer Greedy Behavior in IEEE 802.11 Hotspots , 2006, IEEE Transactions on Mobile Computing.

[31]  Srikanth V. Krishnamurthy,et al.  Denial of Service Attacks in Wireless Networks: The Case of Jammers , 2011, IEEE Communications Surveys & Tutorials.

[32]  Patrick Tague,et al.  How to jam without getting caught: Analysis and empirical study of stealthy periodic jamming , 2013, 2013 IEEE International Conference on Sensing, Communications and Networking (SECON).

[33]  Guevara Noubir,et al.  Linear programming models for jamming attacks on network traffic flows , 2008, 2008 6th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops.

[34]  Tuomas Sandholm,et al.  Computing an approximate jam/fold equilibrium for 3-player no-limit Texas Hold'em tournaments , 2008, AAMAS.

[35]  Douglas A. Gray,et al.  Anti-jamming techniques for multichannel SAR imaging , 2006 .

[36]  Tansu Alpcan,et al.  Network Security , 2010 .

[37]  Andrey Garnaev,et al.  An Eavesdropping Game with SINR as an Objective Function , 2009, SecureComm.

[38]  Eitan Altman,et al.  A Jamming Game in Wireless Networks with Transmission Cost , 2007, NET-COOP.

[39]  Matthias Nussbaum Principles Of Secure Communication Systems , 2016 .

[40]  Eitan Altman,et al.  Jamming in wireless networks: The case of several jammers , 2009, 2009 International Conference on Game Theory for Networks.