A Technique for PUE Detection and Isolation in Cognitive Radio Network

Full Text (PDF, 858KB), PP.14-25

Views: 0 Downloads: 0

Author(s)

Samuel A. Adebo 1 Elizabeth N. Onwuka 1 Abraham U. Usman 1 Supreme Ayewoh Okoh 2,* Okwudili Onyishi 3

1. Department of Telecommunication Engineering, Federal University of Technology Minna, Nigeria

2. Department of Software Engineering, Veritas University Abuja, Nigeria

3. Department of Electrical and Electronics Engineering, Federal University of Technology Minna, Nigeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijwmt.2023.03.02

Received: 14 Nov. 2022 / Revised: 12 Dec. 2022 / Accepted: 7 Mar. 2023 / Published: 8 Jun. 2023

Index Terms

Spectrum, user, isolation, emulator, attack

Abstract

The primary aim of a cognitive radio (CR) system is to optimize spectrum usage by exploiting the existing spectrum holes. Nevertheless, the success of cognitive radio technology is significantly threatened by the primary user emulation attack (PUEA). A rogue secondary user (SU) known as the primary user emulator (PUE) impersonates a legitimate primary user (PU) in a PUEA, thereby preventing other SUs from accessing the spectrum holes. Which leads to the decrease in quality of service (QoS), connection undependability, degraded throughput, energy depletion, and the network experiences a deterioration in its overall performance. In order to alleviate the impact of PUEA on Cognitive Radio Networks (CRNs), it is necessary to detect and isolate the threat agent (PUE) from the network. In this paper, a method for finding and isolating the PUE is proposed. MATLAB simulation results showed that the presence of PUE caused a significant decrease in the throughput of SUs, from to . The throughput was highest at a false alarm (FA) probability of 0.0, indicating no PUE, and decreased as the FA probability increased. At a FA probability of 1, the throughput reached zero, indicating complete takeover of the spectrum by PUE. By isolating the PUE from the network, the other SUs can access the spectrum holes, leading to increased QoS, connection reliability, improved throughput, and efficient energy usage. The presented technique is an important step towards enhancing the security and reliability of CRNs.

Cite This Paper

Samuel A. Adebo, Elizabeth N. Onwuka, Abraham U. Usman, Supreme Ayewoh Okoh, Okwudili Onyishi, "A Technique for PUE Detection and Isolation in Cognitive Radio Network", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.13, No.3, pp. 14-25, 2023. DOI:10.5815/ijwmt.2023.03.02

Reference

[1]M. Ozger and O. B. Akan, “On the utilization of spectrum opportunity in cognitive radio networks,” IEEE Communications Letters, vol. 20, no. 1. pp. 157–160, 2016.
[2]P. Goyal, A. S. Buttar, and M. Goyal, “An efficient spectrum hole utilization for transmission in Cognitive Radio Networks,” in 3rd International Conference on Signal Processing and Integrated Networks, SPIN 2016, 2016, pp. 322–327.
[3]K. Yadav, S. D. Roy, and S. Kundu, “Total error reduction in presence of malicious user in a cognitive radio network,” in 2018 2nd International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2018, 2018, pp. 4–7.
[4]X. Li, G. Xie, and J. Gao, “Detection efficiency analysis of cooperative spectrum sensing in cognitive radio networks,” 2018 IEEE 4th Int. Conf. Comput. Commun. ICCC 2018, vol. 15, no. 3, pp. 462–467, 2018.
[5]S. Althunibat, M. Di Renzo, and F. Granelli, “Cooperative spectrum sensing for cognitive radio networks under limited time constraints,” Comput. Commun., vol. 43, pp. 55–63, 2014.
[6]I. F. Akyildiz, B. F. Lo, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: A survey,” Phys. Commun., vol. 4, no. 1, pp. 40–62, 2011.
[7]H. Du, S. Fu, and H. Chu, “A Credibility-Based Defense SSDF Attacks Scheme for the Expulsion of Malicious Users in Cognitive Radio,” Int. J. Hybrid Inf. Technol., vol. 8, no. 9, pp. 269–280, 2015.
[8]C. K. Yu, M. Van Der Schaar, and A. H. Sayed, “Distributed spectrum sensing in the presence of selfish users,” 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013. pp. 392–395, 2013.
[9]G. Sharma and R. Sharma, “Distributed cooperative spectrum sensing over different fading channels in cognitive radio,” in 2017 International Conference on Computer, Communications and Electronics, COMPTELIX 2017, 2017, pp. 107–111.
[10]W. Khalid and H. Yu, “Optimal sensing performance for cooperative and non-cooperative cognitive radio networks,” International Journal of Distributed Sensor Networks, vol. 13, no. 11. 2017.
[11]M. G. Khoshkholgh, K. Navaie, and H. Yanikomeroglu, “Outage performance of the primary service in spectrum sharing networks,” IEEE Transactions on Mobile Computing, vol. 12, no. 10. pp. 1955–1971, 2013.
[12]S. Shrivastava and D. P. Kothari, “SU throughput enhancement in a decision fusion based cooperative sensing system,” AEU - Int. J. Electron. Commun., vol. 87, no. January, pp. 95–100, 2018.
[13]A. Ashokan and L. Jacob, “Distributed cooperative spectrum sensing with multiple coalitions and non-ideal reporting channel,” in 2017 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2017, 2017, pp. 1–6.
[14]R. Sultana and M. Hussain, “Mitigating primary user emulation attack in cognitive radio network using localization and variance detection,” in Smart Innovation, Systems and Technologies, 2018, vol. 79, pp. 433–444.
[15]N. Saeed, H. Nam, T. Y. Al-Naffouri, and M. S. Alouini, “Primary user localization and its error analysis in 5G cognitive radio networks,” Sensors (Switzerland), vol. 19, no. 9. 2019.
[16]Z. El Mrabet, Y. Arjoune, H. El Ghazi, B. A. Al Majd, and N. Kaabouch, “Primary user emulation attacks: A detection technique based on kalman filter,” Journal of Sensor and Actuator Networks, vol. 7, no. 3. 2018.
[17]R. Yu, Y. Zhang, Y. Liu, S. Gjessing, and M. Guizani, “Securing cognitive radio networks against primary user emulation attacks,” IEEE Network, vol. 30, no. 6. pp. 62–69, 2016.
[18]K. Philemon Dawar, A. U. Usman, B. Alhaji Salihu, M. David, S. Ayewoh Okoh, and A. Ajiboye, “Comparative Analysis of Macro-Femto Networks Interference Mitigation Techniques,” Int. J. Wirel. Microw. Technol., vol. 12, no. 6, pp. 14–24, Dec. 2022.
[19]N. Nguyen Thanh, P. Ciblat, A. T. Pham, and V. T. Nguyen, “Surveillance Strategies Against Primary User Emulation Attack in Cognitive Radio Networks,” IEEE Trans. Wirel. Commun., vol. 14, no. 9, pp. 4981–4993, 2015.
[20]N. T. Nguyen, R. Zheng, and Z. Han, “On identifying primary user emulation attacks in cognitive radio systems using nonparametric Bayesian classification,” IEEE Trans. Signal Process., vol. 60, no. 3, pp. 1432–1445, 2012.
[21]D. Ta et al., “Mitigating Primary Emulation Attacks in Multi-Channel Cognitive Radio Networks : A Surveillance Game To cite this version : HAL Id : hal-01713280 Mitigating primary emulation attacks in multi-channel cognitive radio networks : A surveillance game,” 2018.
[22]M. García-Otero and A. Población-Hernández, “Location Aided Cooperative Detection of Primary User Emulation Attacks in Cognitive Wireless Sensor Networks Using Nonparametric Techniques,” J. Sensors, vol. 2016, 2016.
[23]D. Pu and A. M. Wyglinski, “Primary-user emulation detection using database-assisted frequency-domain action recognition,” IEEE Trans. Veh. Technol., vol. 63, no. 9, pp. 4372–4382, 2014.
[24]J. A. Betances, “Physical Layer Defenses Against Primary User Emulation Attacks,” p. 109, 2016.
[25]S. Arulselvi, “Higher Order Statics based Primary User Emulation Attack Detection,” Indian J. Sci. Technol., vol. 8, no. 32, 2015.
[26]A. Jayapalan and T. Karuppasamy, “Spectrum Sensing and Mitigation of Primary User Emulation Attack in Cognitive Radio,” in Cognitive Radio in 4G/5G Wireless Communication Systems, S. S. Moghaddam, Ed. Rijeka: IntechOpen, 2018.
[27]S. A. Adebo, E. N. Onwuka, A. U. Usman, and A. J. Onumanyi, “A hybrid localization scheme for detection of primary user emulator in cognitive radio networks,” International Journal of Computing and Digital Systems, vol. 8, no. 3. pp. 217–227, 2019.
[28]Y. C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Trans. Wirel. Commun., vol. 7, no. 4, pp. 1326–1337, 2008.
[29]S. Stotas and A. Nallanathan, “On the throughput maximization of spectrum sharing cognitive radio networks,” in GLOBECOM - IEEE Global Telecommunications Conference, 2010, pp. 0–4.
[30]P. R. Lin, Y. Z. Chen, P. H. Chang, and S. S. Jeng, “Cooperative spectrum sensing and optimization on multi-Antenna energy detection in Rayleigh fading channel,” in 2018 27th Wireless and Optical Communication Conference, WOCC 2018, 2018, pp. 1–5.