Generic placeholder image

International Journal of Sensors, Wireless Communications and Control

Author(s): Doaa Kiwan, John P. Fonseka and Rana A. Hassan*

DOI: 10.2174/2210327911666210201104628

DownloadDownload PDF Flyer Cite As
Comparing Adversary Defense Mechanisms in Cognitive Radio Networks

Page: [178 - 183] Pages: 6

  • * (Excluding Mailing and Handling)

Abstract

Background: In a cognitive radio network, the cognitive transmitter senses the medium to detect spectrum opportunities and transmits its own data if the channel is sensed to be idle. A jammer can also sense the medium and identify the slots of successful transmission. The jammer’s main objective is to reduce the throughput of the cognitive transmitter.

Methods: Towards this objective, the jammer builds a deep learning classifier in which the most recent sensing results of acknowledgments (ACKs) sent by the receiver are used to predict the slots of successful transmissions of the cognitive transmitter. This allows the attacker to reliably predict the successful transmissions and can effectively jam these transmissions. The deep learning classification soft decision probabilities are used by the jammer for power control subject to a certain power budget. A receiverbased defense mechanism is developed against jamming attacks. The receiver purposely takes some wrong actions, i.e., sends ACK when the transmission is not successful and vice versa, to poison the training process of the attacker.

Results: We show that our receiver’s defense mechanism effectively enhances the throughput of the cognitive transmitter by about 25% when compared to the transmitter’s defense mechanism, where the transmitter takes some wrong decisions when it accesses the medium.

Conclusion: A novel defense mechanism against jamming attacks in cognitive radio networks is introduced.

Keywords: Cognitive radio, deep learning, throughput, jammer, defense mechanisms, ACK.

Graphical Abstract

[1]
Haykin S. Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Comm 2005; 23(2): 201-20.
[http://dx.doi.org/10.1109/JSAC.2004.839380]
[2]
Wang B, Liu K. Advances in cognitive radio networks: A survey. IEEE J Sel Top Signal Process 2010; 5(1): 5-23.
[http://dx.doi.org/10.1109/JSTSP.2010.2093210]
[3]
Biglieri E, Goldsmith A, Greenstein L, Poor H, Mandayam N. Principles of cognitive radio. Cambridge University Press 2013.
[4]
Xu W, Trappe W, Zhang Y, Wood T. The feasibility of launching and detecting jamming attacks in wireless networks. Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing. 46-57.
[http://dx.doi.org/10.1145/1062689.1062697]
[5]
Bayraktaroglu E, King C, Liu X, Noubir G, Rajaraman R, Thapa B. Performance of IEEE 802.11 under jamming. Mob Netw Appl 2013; 18(5): 678-96.
[http://dx.doi.org/10.1007/s11036-011-0340-4]
[6]
Shi Y, Erpek T, Sagduyu Y, Li J. Spectrum data poisoning with adversarial deep learning. MILCOM 2018-2018 IEEE Military Communications Conference. 407-12.
[http://dx.doi.org/10.1109/MILCOM.2018.8599832]
[7]
Erpek T, Sagduyu Y, Shi Y. Deep learning for launching and mitigating wireless jamming attacks. IEEE Transactions on Cognitive Communications and Networking 2018; 5(1): 2-14.
[http://dx.doi.org/10.1109/TCCN.2018.2884910]
[8]
Liu X, Yang D, El Gamal A. Deep neural network architectures for modulation classification. 51st Asilomar Conference on Signals, Sys-tems, and Computers 915-9.
[http://dx.doi.org/10.1109/ACSSC.2017.8335483]
[9]
Lee W, Kim M, Cho D. Deep cooperative sensing: Cooperative spectrum sensing based on convolutional neural networks. IEEE Trans Vehicular Technol 2019; 68(3): 3005-9.
[http://dx.doi.org/10.1109/TVT.2019.2891291]
[10]
Ye H, Li GY, Juang B. Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wirel Commun Lett 2017; 7(1): 114-7.
[http://dx.doi.org/10.1109/LWC.2017.2757490]
[11]
Arora S, Singh S. Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 2019; 23(3): 715-34.
[http://dx.doi.org/10.1007/s00500-018-3102-4]
[12]
Wang G, Deb S, Coelho L. Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. Int J Bio-inspired Comput 2018; 12(1): 1-22.
[http://dx.doi.org/10.1504/IJBIC.2018.093328]
[13]
Wang G, Deb S, Gao X, Coelho L. A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. Int J Bio-inspired Comput 2016; 8(6): 394-409.
[http://dx.doi.org/10.1504/IJBIC.2016.081335]
[14]
Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl Base Syst 2015; 89: 228-49.
[http://dx.doi.org/10.1016/j.knosys.2015.07.006]