Abstract
In wireless communication, there is an enormous increase in the number of users which results in spectrum shortage. This can be defeated through dynamic spectrum access scheme and cognitive radio is the best solution to achieve this. A cognitive radio (CR) is programmable, configurable and can dynamically use the spectrum in the wireless environment. One of the important tasks of CR is to perform spectrum sensing. Sensing done on spectrum facilitates the cognitive radio user to find the unoccupied area of the spectrum. This CR is prone to many attacks in the wireless environment like primary user emulation attack (PUEA), denial of service attack, replay attack, etc. Among them, the most important attack is PUEA. When a user mimics the primary user and acquires priority over other users to access the spectrum then it is termed as PUEA. In this work, PUEA detection and its defense techniques have been modeled to avoid the degradation of spectrum. To detect the malicious users feature detection-based sensing with double threshold has been proposed. If the detected signal falls above the upper threshold or below the lower threshold then it is considered as primary user signal, whereas if the signal falls in between the thresholds then that signal is identified as malicious user signal. The detection of PUEA cannot be accurate due to factors like false alarm and miss detection. Hence, a game model has been designed for the legitimate nodes to reach strategic defense decisions in the presence of multiple attackers. Based on the actions of the players, the defenders’ payoff is determined to obtain optimal defense decisions.
Similar content being viewed by others
References
Mitola, J., III.: Cognitive radio for flexible mobile multimedia communications. In: Mobile Networks and Applications, pp. 435–441 (2001)
Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)
Bayrakdar, M.E.; Caihan, A.: Artificial bee colony-based spectrum handoff algorithm in wireless cognitive radio networks. Int. J. Commun. Syst. 31, e3495 (2018)
Igbinosa, I.E.; Oyerinde, O.O.; Srivastava, V.M.; Mneney, S.: Spectrum sensing methodologies for cognitive radio systems: a review. Int. J. Adv. Comput. Sci. Appl. 6(12), 13–22 (2015)
Zhou, X.; Jing, X.; Huang, H.; Li, J.: An enhanced double threshold energy detection in cognitive radio. In: International Conference on Signal and Information Processing, Networking and Computers, pp. 80–86 (2017)
Zhu, J.; Xu, Z.; Wang, F.; Huang, B.; Zhang, B.: Double threshold energy detection of cooperative spectrum sensing in cognitive radio. In: 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (2008). https://doi.org/10.1109/CROWNCOM.2008.4562451
Kumar, A.; Thakur, P.; Pandit, S.; Singh, G.: Analysis of optimal threshold selection for spectrum sensing in a cognitive radio network: an energy detection approach. Wirel. Netw. 25, 3917–3931 (2019)
Krishnana, K.V.; Sajitha, R.M.; Kharab, S.: Dynamic resource allocation in OFDM based cognitive radio system considering primary user QoS and secondary user proportional constraints. J. Commun. Technol. Electron. 60, 1269–1275 (2015)
Khan, I.; Singh, P.: Double threshold feature detector for cooperative spectrum sensing in cognitive radio networks. In: IEEE India Conference (INDICON) (2015). https://doi.org/10.1109/INDICON.2014.7030573
Ivanov, A.: Feature extraction in local spectrum sensing for next generation cognitive radios—a review. J. Mobile Multimed. 15(1–2), 111–140 (2020)
Kakalou, I.; Psannis, K.E.: Coordination without collaboration in imperfect games: the primary user emulation attack example. IEEE Access 6, 5402–5414 (2018)
Pei, Y.; Liang, Y.; Zhang, L.; The, K.C.; Li, K.H.: Secure communication over MISO cognitive radio channels. IEEE Trans. Wirel. Commun. 9, 1494–1502 (2010)
Fudenberg, D.; Tirole, J.: Game Theory. MIT Press, Cambridge (1991)
Wan, R.; Ding, L.; Xiong, N.; Shu, W.; Yang, L.: Dynamic dual threshold cooperative spectrum sensing for cognitive radio under noise power uncertainity. Hum. Centric Comput. Inf. Sci. 9, 1–21 (2019)
Kumar, A.; Kumar, N.: OFDM system with cyclostationary feature detection spectrum sensing. ICT Express (2019). https://doi.org/10.1016/j.icte.2018.01.007
Felegyhazi, M.; Hubaux, J.: Game theory in wireless networks: a tutorial. Technical Report, pp. 1–16 (2006)
Liang, X.; Xiao, Y.: Game theory for network security. IEEE Commun. Surv. Tutor. 15, 472–486 (2013)
Wang, Y.; Yu, F.; Tang, H.; Huang, M.: A mean field game theoretic approach for security enhancements in mobile ad hoc networks. IEEE Transa. Wirel. Commun. 13, 1616–1627 (2014)
Elnouran, M.G.A.: Cognitive Radio and Game Theory: Overview and Simulation. Blekinge Institute of Technology, Karlskrona (2008)
Whalen, A.: Statistical theory of signal detection and parameter estimation. IEEE Commun. Mag. 22, 37–44 (1984)
Li, F.; Wu, J.: Hit and run: a Bayesian game between malicious and regular nodes in MANETS. In: 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Adhoc Communications and Networks, pp. 432–440 (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jayapalan, A., Savarinathan, P., Chenna Reddy, J. et al. Detection and Defense of PUEA in Cognitive Radio Network. Arab J Sci Eng 46, 4039–4048 (2021). https://doi.org/10.1007/s13369-020-05278-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-020-05278-9