Smart Jamming Attacks in Wireless Networks During a Transmission Cycle: Stackelberg Game with Hierarchical Learning Solution

Due to the broadcast nature of the shared medium, wireless communications become more vulnerable to malicious attacks. In this paper, we tackle the problem of jamming in wireless network when the transmission of the jammer and the transmitter occur with a non-zero cost. We focus on a jammer who keeps track of the re-transmission attempts of the packet until it is dropped. Firstly, we consider a power control problem following a Nash Game model, where all players take action simultaneously. Secondly, we consider a Stackelberg Game model, in which the transmitter is the leader and the jammer is the follower. As the jammer has the ability to sense the transmission power, the transmitter adjusts its transmission power accordingly, knowing that the jammer will do so. We provide the closed-form expressions of the equilibrium strategies where both the transmitter and the jammer have a complete information. Thereafter, we consider a worst case scenario where the transmitter has an incomplete information while the jammer has a complete information. We introduce a Reinforcement Learning method, thus, the transmitter can act autonomously in a dynamic environment without knowing the above Game model. It turns out that despite the jammer ability of sensing the active channel, the transmitter can enhance its efficiency by predicting the jammer reaction according to its own strategy.

[1]  Pengfei Zhang,et al.  Localizing Wireless Jamming Attacks with Minimal Network Resources , 2017, SpaCCS Workshops.

[2]  Liang Xiao,et al.  Power control Stackelberg game in cooperative anti-jamming communications , 2014, The 2014 5th International Conference on Game Theory for Networks.

[3]  Thierry Turletti,et al.  Performance analysis of the IEEE 802.11 MAC and physical layer protocol , 2005, Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks.

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

[5]  R. Michael Buehrer,et al.  On Jamming Against Wireless Networks , 2015, IEEE Transactions on Wireless Communications.

[6]  Alagan Anpalagan,et al.  A Hierarchical Learning Solution for Anti-Jamming Stackelberg Game With Discrete Power Strategies , 2017, IEEE Wireless Communications Letters.

[7]  Michele Zorzi,et al.  On the randomization of transmitter power levels to increase throughput in multiple access radio systems , 1998, Wirel. Networks.

[8]  Stevan M. Berber,et al.  Performance Analysis of Chaotic DSSS-CDMA Synchronization Under Jamming Attack , 2016, Circuits Syst. Signal Process..

[9]  Jens C. Arnbak,et al.  Capacity of Slotted ALOHA in Rayleigh-Fading Channels , 1987, IEEE J. Sel. Areas Commun..

[10]  Yonggang Zhu,et al.  Bayesian Stackelberg Game for Antijamming Transmission With Incomplete Information , 2016, IEEE Communications Letters.

[11]  Xiaohu Ge,et al.  Energy Efficiency Challenges of 5G Small Cell Networks , 2017, IEEE Communications Magazine.

[12]  Abdelkrim Haqiq,et al.  An efficient pricing mechanism of random access in wireless network with self-interested mobile users , 2015, 2015 International Conference on Wireless Networks and Mobile Communications (WINCOM).

[13]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[14]  Xiaohua Ge,et al.  Distributed Attack Detection and Secure Estimation of Networked Cyber-Physical Systems Against False Data Injection Attacks and Jamming Attacks , 2018, IEEE Transactions on Signal and Information Processing over Networks.

[15]  Xiaohua Ge,et al.  Distributed Secure Estimation Over Wireless Sensor Networks Against Random Multichannel Jamming Attacks , 2017, IEEE Access.

[16]  Zoran Hadzi-Velkov,et al.  Capture effect in IEEE 802.11 basic service area under influence of Rayleigh fading and near/far effect , 2002, The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

[17]  Yan Li,et al.  Power control with reinforcement learning in cooperative cognitive radio networks against jamming , 2015, The Journal of Supercomputing.

[18]  Radia J. Perlman,et al.  Network security - private communication in a public world , 2002, Prentice Hall series in computer networking and distributed systems.

[19]  Changyin Sun,et al.  Energy efficient jamming attack schedule against remote state estimation in wireless cyber-physical systems , 2018, Neurocomputing.

[20]  Xianglin Wei,et al.  Collaborative mobile jammer tracking in Multi-Hop Wireless Network , 2018, Future Gener. Comput. Syst..

[21]  Monika Sachdeva,et al.  Detection and Prevention of DDoS Attacks in Wireless Sensor Networks , 2018 .

[22]  Xi Fang,et al.  Optimal transmission power control in the presence of a smart jammer , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[23]  Young-Hyun Oh,et al.  Asynchronous Channel-Hopping Scheme under Jamming Attacks , 2018, Mob. Inf. Syst..

[24]  Vinod Sharma,et al.  Performance analysis of a slotted-ALOHA protocol on a capture channel with fading , 1999, Queueing Syst. Theory Appl..

[25]  Mahbub Hassan,et al.  Service differentiation in wireless LANs based on capture , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[26]  Bharat Bhushan,et al.  Security vulnerabilities and countermeasures against jamming attacks in Wireless Sensor Networks: A survey , 2017, 2017 International Conference on Computer, Communications and Electronics (Comptelix).