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Mitigation of primary user emulation attack using a new energy detection method in cognitive radio networks

一种新型能量检测方法降低认知无线电网络中的主用户仿真攻击

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Abstract

A most promising solution to the expansion of spectrum efficiency is cognitive radio (CR) and this expansion is achieved by permitting the licensed frequency bands to be accessed by unlicensed secondary users (SUs) with a lack of interference with licensed primary users (PUs). This utilization of CR networks in the spectrum sensing causes vulnerable attacks like primary user emulation (PUE) attack and here PUs play the role of malicious user and do not permit other users to utilize PUs channel even in their unavailability. On the basis of the traditional single-threshold energy detection algorithm, a novel modified double-threshold energy detector is formulated in the CR network and the detection probability, miss detection probability, probability of false alarm, and their inter-relationship are analyzed. This paper develops a modified double threshold energy detection cooperative spectrum sensing technique to alleviate the PUE attack. Finally, performance-based evaluation is carried out between the proposed and the existing energy detection spectrum sensing method that had no consideration on PUE attack. The resultant of the simulation in MATLAB has revealed that the proposed model has significantly mitigated PUE attack by means of providing outstanding performance.

摘要

扩展频谱效率最有前途的解决方案是认知无线电(CR)技术。这种扩展是通过允许未经许可的辅助用户(SUs)访问许可的频段来实现的,不存在对许可的主用户(PUs)的干扰。这种在频谱感知中对CR网络的使用会导致脆弱的攻击,如主用户仿真(PUE)攻击,此时,PUs扮演了恶意用户的角色,不允许其他用户即使在他们不可用的情况下使用PUs 通道。在传统的单阈值能量检测算法的基础上,在CR网络中建立一种改进的双阈值能量检测器,对检测概率、漏检概率、误报概率及其相互关系进行检测分析。提出一种改进的双阈值能量检测协同频谱传感技术,以缓解对主要用户的攻击。并对所提出的方法与现有的不考虑提示攻击的能量检测频谱传感方法基于性能方面进行对比评价。在MATLAB的仿真结果表明,所提出的能量检测协同频谱传感技术性能出色,显著减轻了对主用户的攻击。

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Abbreviations

CR:

Cognitive radio

DOA:

Directions of arrival

PUs:

Primary users

SUs:

Secondary users

PUE:

Primary user emulation

CSS:

Cooperative spectrum sensing

EHMS:

Embedded health monitoring systems

WCS:

Wireless communication services

SSD:

Signal strength distribution

CDF:

Cumulative distribution function

SNR:

Signal to noise ratio

RSS:

Received signal strength

HV:

Half voting

GPS:

Global positioning system

CRNN:

Convolutional recurrent neural network

FC:

Fibre channel

ROC:

Receiver operating characteristics

CSSED:

Cooperative spectrum sensing energy detector

AWGN:

Additive white Gaussian noise

References

  1. JAGTAP A M, GOMATHI N. Improved Salp swarm algorithm for network connectivity in mobile sensor network [J]. Journal of Networking and Communication Systems (JNACS), 2019, 2(3): 11–19. DOI: https://doi.org/10.46253/jnacs.v2i3.a2.

    Google Scholar 

  2. BRAJULA W, PRAVEENA S. Energy efficient genetic algorithm based clustering technique for prolonging the life time of wireless sensor network [J]. Journal of Networking and Communication Systems (JNACS), 2018, 1(1): 1–9. DOI: https://doi.org/10.46253/jnacs.v1i1.a1.

    Google Scholar 

  3. FEI H B A W. Student information management system (SIMS) [J]. International Journal of Computer Engineering & Technology (IJCET), 2014, 5(2): 9–18.

    Google Scholar 

  4. ABDALLA H B, LIN **-zhao, LI Guo-quan. NoSQL: Collection document and cloud by using a dynamic web query form [C]//Proc SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015). 2015, 9631: 534–542. DOI: https://doi.org/10.1117/12.2197093.

    Google Scholar 

  5. GHAZNAVI M, JAMSHIDI A. A reliable spectrum sensing method in the presence of malicious sensors in distributed cognitive radio network [J]. IEEE Sensors Journal, 2015, 15(3): 1810–1816. DOI: https://doi.org/10.1109/JSEN.2014.2366642.

    Google Scholar 

  6. WAN Run-ze, DING Li-xin, XIONG Nai-xue, et al. Dynamic dual threshold cooperative spectrum sensing for cognitive radio under noise power uncertainty [J]. Human-Centric Computing and Information Sciences, 2019, 9: 22. DOI: https://doi.org/10.1186/s13673-019-0181-x.

    Article  Google Scholar 

  7. WU Jun, WANG Cong, YU Yue, et al. Performance optimisation of cooperative spectrum sensing in mobile cognitive radio networks [J]. IET Communications, 2020, 14(6): 1028–1036. DOI: https://doi.org/10.1049/iet-com.2019.1083.

    Article  Google Scholar 

  8. HALDORAI A, KANDASWAMY U. Cooperative spectrum handovers in cognitive radio networks intelligent [R]. 2019. DOI: https://doi.org/10.1007/978-3-030-15416-5_1.

  9. CHAKRABORTY A, BANERJEE J S, CHATTOPADHYAY A. Non-uniform quantized data fusion rule for data rate saving and reducing control channel overhead for cooperative spectrum sensing in cognitive radio networks [J]. Wireless Personal Communications, 2019, 104(2): 837–851. DOI: https://doi.org/10.1007/s11277-018-6054-1.

    Article  Google Scholar 

  10. E. D. N. FCC. 03–222. Notice of proposed rule making and order [S]. 2003.

  11. McHENRY M. Spectrum white space measurements [R]. New America Foundation Broadband Forum, 2003.

  12. AKYILDIZ I F, LEE W Y, VURAN M C, et al. NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey [J]. Computer Networks, 2006, 50(13): 2127–2159. DOI: https://doi.org/10.1016/j.comnet.2006.05.001.

    Article  Google Scholar 

  13. QI Yuan, PENG Tao, WANG Wen-bo, et al. Cyclostationarity-based spectrum sensing for wideband cognitive radio [C]//2009 WRI International Conference on Communications and Mobile Computing. Kunming, China: IEEE, 2009: 107–111. DOI: https://doi.org/10.1109/CMC.2009.299.

    Google Scholar 

  14. KIM H, SHIN K G. In-band spectrum sensing in cognitive radio networks: Energy detection or feature detection? [C]//Proceedings of the 14th ACM international conference on Mobile computing and networking-MobiCom’ 08. New York: ACM Press, 2008. DOI: https://doi.org/10.1145/1409944.1409948.

    Google Scholar 

  15. WILD B, RAMCHANDRAN K. Detecting primary receivers for cognitive radio applications [C]//First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Baltimore, MD, USA: IEEE, 2005: 124–130. DOI: https://doi.org/10.1109/DYSPAN.2005.1542626.

    Google Scholar 

  16. GAO Yue-hong, JIANG Yu-ming. Performance analysis of a cognitive radio network with imperfect spectrum sensing [C]//2010 INFOCOM IEEE Conference on Computer Communications Workshops. San Diego, CA, USA: IEEE, 2010. DOI: https://doi.org/10.1109/infcomw.2010.5466711.

    Google Scholar 

  17. SETOODEH P, HAYKIN S, MOGHADAM K R. Dynamic spectrum supply chain model for cognitive radio networks [C]//2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). 2012: 1–6. DOI: https://doi.org/10.1109/WoWMoM.2012.6263756.

  18. REN Zi-yang, TAGHIPOUR A, CANEL-DEPITRE B. Information sharing in supply chain under uncertainty [C]//2016 6th International Conference on Information Communication and Management (ICICM). Hatfield, UK: IEEE, 2016: 67–71. DOI: https://doi.org/10.1109/INFOCOMAN.2016.7784217.

    Chapter  Google Scholar 

  19. VOSOOGHIDIZAJI M, TAGHIPOUR A, CANEL-DEPITRE B. Supply chain coordination under information asymmetry: A review [J]. International Journal of Production Research, 2020, 58(6): 1805–1834. DOI: https://doi.org/10.1080/00207543.2019.1685702.

    Article  Google Scholar 

  20. SARALA B, DEVI S R, SHEELA J J J. Spectrum energy detection in cognitive radio networks based on a novel adaptive threshold energy detection method [J]. Computer Communications, 2020, 152: 1–7. DOI: https://doi.org/10.1016/j.comcom.2019.12.058.

    Article  Google Scholar 

  21. KUMAR A, THAKUR P, PANDIT S, et al. Analysis of optimal threshold selection for spectrum sensing in a cognitive radio network: An energy detection approach [J]. Wireless Networks, 2019, 25(7): 3917–3931. DOI: https://doi.org/10.1007/s11276-018-01927-y.

    Article  Google Scholar 

  22. SARALA B, DEVI D R, BHARGAVA D S. Classical energy detection method for spectrum detecting in cognitive radio networks by using robust augmented threshold technique [J]. Cluster Computing, 2019, 22(5): 11109–11118. DOI: https://doi.org/10.1007/s10586-017-1311-8.

    Article  Google Scholar 

  23. GHAZNAVI M, JAMSHIDI A. Efficient method for reducing the average control bits in a distributed cooperative sensing in cognitive radio system [J]. IET Communications, 2013, 7(9): 867–874. DOI: https://doi.org/10.1049/iet-com.2012.0574.

    Article  Google Scholar 

  24. JAMSHIDI A. Performance analysis of low average reporting bits cognitive radio schemes in bandwidth constraint control channels [J]. IET Communications, 2009, 3(9): 1544. DOI:https://doi.org/10.1049/iet-com.2008.0507.

    Article  Google Scholar 

  25. CHEN R, PARK J M. Ensuring trustworthy spectrum sensing in cognitive radio networks [C]//IEEE Workshop on Networking Technologies for Software Defined Radio Networks. 2006: 110–119. DOI: https://doi.org/10.1109/SDR.2006.4286333.

  26. GHOSH S K, MEHEDI J, SAMAL U C. Sensing performance of energy detector in cognitive radio networks [J]. International Journal of Information Technology, 2019, 11(4): 773–778. DOI: https://doi.org/10.1007/s41870-018-0236-7.

    Article  Google Scholar 

  27. SAJID A, KHALID B, ALI M, et al. Securing cognitive radio networks using blockchains [J]. Future Generation Computer Systems, 2020, 108: 816–826. DOI: https://doi.org/10.1016/j.future.2020.03.020.

    Article  Google Scholar 

  28. LI Yong-cheng, MA **ang-rong, WANG Man-xi, et al. Detecting primary user emulation attack based on multipath delay in cognitive radio network [C]//Smart Innovations in Communication and Computational Sciences. 2019: 361–373. DOI: https://doi.org/10.1007/978-981-10-8968-8_31.

  29. GHAZNAVI M, JAMSHIDI A. Interference impact on the outage capacity of a frequency diversity paradigm in cognitive radio networks [J]. IET Communications, 2012, 6(2): 179–186. DOI: https://doi.org/10.1049/iet-com.2011.0075.

    Article  MathSciNet  Google Scholar 

  30. JIN Z, ANAND S, SUBBALAKSHMI K P. Detecting primary user emulation attacks in dynamic spectrum access networks [C]//2009 IEEE International Conference on Communications. Dresden, Germany: IEEE, 2009: 1–5. DOI: https://doi.org/10.1109/ICC.2009.5198911.

    Google Scholar 

  31. CHEN Rui-liang, PARK J M, REED J H. Defense against primary user emulation attacks in cognitive radio networks [J]. IEEE Journal on Selected Areas in Communications, 2008, 26(1): 25–37. DOI: https://doi.org/10.1109/JSAC.2008.080104.

    Article  Google Scholar 

  32. ROSS S M. Introduction to probability models [M]. Ninth Edition. Academic Press, 2007.

  33. VAZIRI YAZDI S A, GHAZVINI M. Countermeasure with primary user emulation attack in cognitive radio networks [J]. Wireless Personal Communications, 2019, 108(4): 2261–2277. DOI: https://doi.org/10.1007/s11277-019-06521-9.

    Article  Google Scholar 

  34. GUPTA E, POONAM, NAGPAL C K. Survey on PUE attack detection and prevention techniques [J]. International Journal of Emerging Technologies in Engineering Research, 2016, 4(4): 90–95.

    Google Scholar 

  35. FURQAN H M, AYGÜL M A, NAZZAL M, et al. Primary user emulation and jamming attack detection in cognitive radio via sparse coding [J]. EURASIP Journal on Wireless Communications and Networking, 2020, 1: 141. DOI: https://doi.org/10.1186/s13638-020-01736-y.

    Article  Google Scholar 

  36. ZHANG Wei, MALLIK R K, LETAIEF K B. Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2009, 8(12): 5761–5766. DOI: https://doi.org/10.1109/TWC.2009.12.081710.

    Article  Google Scholar 

  37. ATAKLI I, HU H, CHEN Y, et al. Malicious node detection in wireless sensor networks using weighted trust evaluation [C]//Proceedings of the 2008 Spring simulation Multiconference. Society for Computer Simulation International, 2008: 836–843. DOI: https://doi.org/10.1145/1400549.1400686.

  38. ANAND S, JIN Z, SUBBALAKSHMI K P. An analytical model for primary user emulation attacks in cognitive radio networks [C]//3rd IEEE Symposium on new Frontiers in Dynamic Spectrum Access Networks. 2008: 1–6. DOI: https://doi.org/10.1109/DYSPAN.2008.16.

  39. CHEN Ze-sheng, COOKLEV T, CHEN Chao, et al. Modeling primary user emulation attacks and defenses in cognitive radio networks [C]//2009 IEEE 28th International Performance Computing and Communications Conference. Scottsdale, AZ: IEEE, 2009: 208–215. DOI: https://doi.org/10.1109/PCCC.2009.5403815.

    Google Scholar 

  40. BAO Fei-**g, CHEN Hui-fang, XIE Lei. Analysis of primary user emulation attack with motional secondary users in cognitive radio networks [C]//2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications. Sydney, NSW, Australia: IEEE, 2012: 956–961. DOI: https://doi.org/10.1109/PIMRC.2012.6362922.

    Google Scholar 

  41. OMER A E. Review of spectrum sensing techniques in Cognitive Radio networks [C]//2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE). Khartoum, Sudan: IEEE, 2015: 439–446. DOI: https://doi.org/10.1109/ICCNEEE.2015.7381409.

    Google Scholar 

  42. TRIWICAKSONO D, YOUNG-SHIN S. Energy detector and matched filter as cascaded clear channel assessment in wireless network [C]//IET International Conference on Information and Communications Technologies (IETICT 2013). Bei**g, China. Institution of Engineering and Technology, 2013: 551–556. DOI: https://doi.org/10.1049/cp.2013.0100.

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Authors and Affiliations

  1. St. Vincent Pallotti College of Engineering & Technology, Nagpur, Maharashtra, India

    Shriraghavan Madbushi

  2. Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur (Dist.), Guntur, India

    M. S. S. Rukmini

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  1. Shriraghavan Madbushi
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Shriraghavan MADBUSHI provided the concept and edited the draft of the manuscript. M. S. S. RUKMINI conducted the literature review and wrote the first draft of the manuscript. Shriraghavan MADBUSHI edited the draft of the manuscript.

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Correspondence to Shriraghavan Madbushi.

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Shriraghavan MADBUSHI and M. S. S. RUKMINI declare that they have no conflict of interest.

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Madbushi, S., Rukmini, M.S.S. Mitigation of primary user emulation attack using a new energy detection method in cognitive radio networks. J. Cent. South Univ. 29, 1510–1520 (2022). https://doi.org/10.1007/s11771-022-5016-7

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