Log in

Relevant bibliographies by topics / Primary user emulation attacks / Journal articles

Journal articles on the topic 'Primary user emulation attacks'

To see the other types of publications on this topic, follow the link: Primary user emulation attacks.

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Primary user emulation attacks.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Shetty, Sachin, Meena Thanu, and Ravi Ramachandran. "Cognitive Radio: Primary User Emulation Attacks and Remedies." Recent Patents on Computer Sciencee 5, no. 2 (June 1, 2012): 103–8. http://dx.doi.org/10.2174/2213275911205020103.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Lafia, Diafale, Mistura Laide Sanni, Rasheed Ayodeji Adetona, Bodunde Odunola Akinyemi, and Ganiyu Adesola Aderounmu. "Signal Processing-based Model for Primary User Emulation Attacks Detection in Cognitive Radio Networks." Journal of Computing and Information Technology 29, no. 2 (July 4, 2022): 77–88. http://dx.doi.org/10.20532/cit.2021.1005297.

Full text

Abstract:

Cognitive Radio Networks (CRNs) have been conceived to improve the efficiency of accessing the spectrum. However, these networks are prone to various kinds of attacks and failures that can compromise the security and performance of their users. One of the notable malicious attacks in cognitive radio networks is the Primary User Emulation (PUE) attack, which results in underutilization and unavailability of the spectrum and low operational efficiency of the network. This study developed an improved technique for detecting PUE attacks in cognitive radio networks and further addressed the characteristics of sparsely populated cognitive radio networks and the mobility of the primary users. A hybrid signal processing-based model was developed using the free space path loss and additive Gaussian noise models. The free space path loss model was used to detect the position of the transmitter, while the additive Gaussian noise model was used to analyze the signal transmitted, i.e., energy detection in the spectrum at the detected location. The proposed model was benchmarked with an existing model using the number of secondary users and the velocity of the transmitter as performance parameters. The simulation results show that the proposed model has improved accuracy in detecting primary user emulation attacks. It was concluded that the proposed hybrid model with respect to the number of secondary users and the velocity of the transmitter can be used for primary user emulation attack detection in cognitive radio networks.

APA, Harvard, Vancouver, ISO, and other styles

3

Yu, Rong, Yan Zhang, Yi Liu, Stein Gjessing, and Mohsen Guizani. "Securing cognitive radio networks against primary user emulation attacks." IEEE Network 29, no. 4 (July 2015): 68–74. http://dx.doi.org/10.1109/mnet.2015.7166193.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Yu, Rong, Yan Zhang, Yi Liu, Stein Gjessing, and Mohsen Guizani. "Securing Cognitive Radio Networks against Primary User Emulation Attacks." IEEE Network 30, no. 6 (November 2016): 62–69. http://dx.doi.org/10.1109/mnet.2016.1200149nm.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

León, Olga, Juan Hernández-Serrano, and Miguel Soriano. "Cooperative detection of primary user emulation attacks in CRNs." Computer Networks 56, no. 14 (September 2012): 3374–84. http://dx.doi.org/10.1016/j.comnet.2012.05.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Shrivastava, Shivanshu, A. Rajesh, and P. K. Bora. "Defense against primary user emulation attacks from the secondary user throughput perspective." AEU - International Journal of Electronics and Communications 84 (February 2018): 131–43. http://dx.doi.org/10.1016/j.aeue.2017.11.012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Chen, Ruiliang, Jung-Min Park, and Jeffrey H. Reed. "Defense against Primary User Emulation Attacks in Cognitive Radio Networks." IEEE Journal on Selected Areas in Communications 26, no. 1 (January 2008): 25–37. http://dx.doi.org/10.1109/jsac.2008.080104.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Mrabet, Zakaria El, Youness Arjoune, Hassan El Ghazi, Badr Abou Al Majd, and Naima Kaabouch. "Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter." Journal of Sensor and Actuator Networks 7, no. 3 (July 4, 2018): 26. http://dx.doi.org/10.3390/jsan7030026.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Jin, Z., S. Anand, and K. P. Subbalakshmi. "Impact of Primary User Emulation Attacks on Dynamic Spectrum Access Networks." IEEE Transactions on Communications 60, no. 9 (September 2012): 2635–43. http://dx.doi.org/10.1109/tcomm.2012.071812.100729.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Ta, Duc-Tuyen, Nhan Nguyen-Thanh, Patrick Maille, and Van-Tam Nguyen. "Strategic Surveillance Against Primary User Emulation Attacks in Cognitive Radio Networks." IEEE Transactions on Cognitive Communications and Networking 4, no. 3 (September 2018): 582–96. http://dx.doi.org/10.1109/tccn.2018.2826552.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Alcala’ Garrido, Hassel Aurora, Mario E. Rivero-Angeles, and Eleazar Aguirre Anaya. "Primary User Emulation in Cognitive Radio-Enabled WSNs for Structural Health Monitoring: Modeling and Attack Detection." Journal of Sensors 2019 (April 28, 2019): 1–14. http://dx.doi.org/10.1155/2019/6950534.

Full text

Abstract:

Nowadays, the use of sensor nodes for the IoT is widespread. At the same time, cyberattacks on these systems have become a relevant design consideration in the practical deployment of wireless sensor networks (WSNs). However, there are some types of attacks that have to be prevented or detected as fast as possible, like, for example, attacks that put lives in danger. In this regard, a primary user emulation (PUE) attack in a structural health monitoring (SHM) system falls inside this category since nodes failing to report structural damages may cause a collapse of the building with no warning to people inside it. Building on this, we mathematically model an energy and resource utilization-efficient WSN based on the cognitive radio (CR) technique to monitor the SHM of buildings when a seismic activity occurs, making efficient use of scarce bandwidth when a PUE attack is in progress. The main performance metrics considered in this work are average packet delay and average energy consumption. The proposed model allows an additional tool for the prompt identification of such attacks in order to implement effective countermeasures.

APA, Harvard, Vancouver, ISO, and other styles

12

Srinivasan, Sundar, KB Shivakumar, and Muazzam Mohammad. "Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks." International Journal of Distributed Sensor Networks 15, no. 9 (September 2019): 155014771986036. http://dx.doi.org/10.1177/1550147719860365.

Full text

Abstract:

Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc… performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network.

APA, Harvard, Vancouver, ISO, and other styles

13

García-Otero, Mariano, and Adrián Población-Hernández. "Location Aided Cooperative Detection of Primary User Emulation Attacks in Cognitive Wireless Sensor Networks Using Nonparametric Techniques." Journal of Sensors 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/9571592.

Full text

Abstract:

Primary user emulation (PUE) attacks are a major security challenge to cognitive wireless sensor networks (CWSNs). In this paper, we propose two variants of the PUE attack, namely, the relay and replay attacks. Such threats are conducted by malicious nodes that replicate the transmissions of a real primary user (PU), thus making them resilient to many defensive procedures. However, we show that those PUE attacks can be effectively detected by a set of cooperating secondary users (SUs), using location information and received signal strength (RSS) measurements. Two strategies for the detection of PUE relay and replay attacks are presented in the paper: parametric and nonparametric. The parametric scheme is based on the likelihood ratio test (LRT) and requires the existence of a precise path loss model for the observed RSS values. On the contrary, the nonparametric procedure is not tied to any particular propagation model; so, it does not require any calibration process and is robust to changing environmental conditions. Simulations show that the nonparametric detection approach is comparable in performance to the LRT under moderate shadowing conditions, specially in case of replay attacks.

APA, Harvard, Vancouver, ISO, and other styles

14

XIE, Xianzhong, Yuan FANG, and Bin MA. "Robust cooperative spectrum sensing against primary user emulation attacks based on cyclostationarity." SCIENTIA SINICA Informationis 46, no. 6 (April 13, 2016): 789–99. http://dx.doi.org/10.1360/n112015-00168.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Li, Kaili, and Jinting Wang. "Optimal Joining Strategies in Cognitive Radio Networks Under Primary User Emulation Attacks." IEEE Access 7 (2019): 183812–22. http://dx.doi.org/10.1109/access.2019.2957435.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Yuan, Zhou, Dusit Niyato, Husheng Li, Ju Bin Song, and Zhu Han. "Defeating Primary User Emulation Attacks Using Belief Propagation in Cognitive Radio Networks." IEEE Journal on Selected Areas in Communications 30, no. 10 (November 2012): 1850–60. http://dx.doi.org/10.1109/jsac.2012.121102.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Ponnusamy, Vijayakumar, Kottilingam Kottursamy, T. Karthick, M. B. Mukeshkrishnan, D. Malathi, and Tariq Ahamed Ahanger. "Primary user emulation attack mitigation using neural network." Computers & Electrical Engineering 88 (December 2020): 106849. http://dx.doi.org/10.1016/j.compeleceng.2020.106849.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Jin, Z., S. Anand, and K. P. Subbalakshmi. "Mitigating primary user emulation attacks in dynamic spectrum access networks using hypothesis testing." ACM SIGMOBILE Mobile Computing and Communications Review 13, no. 2 (September 25, 2009): 74–85. http://dx.doi.org/10.1145/1621076.1621084.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Saber, M. J., and S. M. S. Sadough. "Optimal soft combination for multiple antenna energy detection under primary user emulation attacks." AEU - International Journal of Electronics and Communications 69, no. 9 (September 2015): 1181–88. http://dx.doi.org/10.1016/j.aeue.2015.04.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Cadena-Muñoz, Ernesto, Luis Fernando Pedraza-Martínez, and Enrique Rodríguez-Colina. "Primary user emulation characterization in mobile cognitive radio networks." Visión electrónica 14, no. 1 (January 31, 2020): 26–34. http://dx.doi.org/10.14483/22484728.16351.

Full text

Abstract:

This paper presents the results of the characterization of the attack known as the "primary user emulation" in mobile cognitive radio networks performing the implementation and testing. The tools and their configuration to carry out the attack are presented and their effects on the network are analyzed. The results show how to generate the attack with a software-defined radio equipment (SDR) using GNU-Radio and OpenBTS. The effects of the possible configurations of the attack on the network are shown, the malicious type generates constant interference on the primary or cognitive network, the selfish type allows to imitate a licensed or primary user generating interference to the primary network and inability to access the Cognitive Network while active. If the emulator's power level is fixed, the services it provides are stable. If the power is variable the services suffer intermittency. Primary user emulation is the attack that most affects the cognitive radio network so its effects are analyzed in order to propose ways of detecting or applying countermeasures.

APA, Harvard, Vancouver, ISO, and other styles

21

Mishra, Nikita, Sumit Srivastava, and Shivendra Nath Sharan. "Countermeasures for Primary User Emulation Attack: A Comprehensive Review." Wireless Personal Communications 115, no. 1 (June 16, 2020): 827–58. http://dx.doi.org/10.1007/s11277-020-07600-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Shrivastava, Shivanshu, Bin Chen, and Hui Wang. "DQN Learning Based Defense Against Smart Primary User Emulation Attacks in Cooperative Sensing Systems." IEEE Access 9 (2021): 163791–814. http://dx.doi.org/10.1109/access.2021.3131339.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Le, Trong Nghia, Wen-Long Chin, and Wei-Che Kao. "Cross-Layer Design for Primary User Emulation Attacks Detection in Mobile Cognitive Radio Networks." IEEE Communications Letters 19, no. 5 (May 2015): 799–802. http://dx.doi.org/10.1109/lcomm.2015.2399920.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Alahmadi, Ahmed, Mai Abdelhakim, Jian Ren, and Tongtong Li. "Defense Against Primary User Emulation Attacks in Cognitive Radio Networks Using Advanced Encryption Standard." IEEE Transactions on Information Forensics and Security 9, no. 5 (May 2014): 772–81. http://dx.doi.org/10.1109/tifs.2014.2310355.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Nguyen, Nam Tuan, Rong Zheng, and Zhu Han. "On Identifying Primary User Emulation Attacks in Cognitive Radio Systems Using Nonparametric Bayesian Classification." IEEE Transactions on Signal Processing 60, no. 3 (March 2012): 1432–45. http://dx.doi.org/10.1109/tsp.2011.2178407.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Xie, Xiongwei, and Weichao Wang. "Detecting Primary User Emulation Attacks in Cognitive Radio Networks via Physical Layer Network Coding." Procedia Computer Science 21 (2013): 430–35. http://dx.doi.org/10.1016/j.procs.2013.09.057.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Lakshmibai, T., and C. Parthasarathy. "Self B - Adaptive Key Generation for Primary Users in Cognitive Radio Networks for Less Prone Primary User Emulation Attacks." International Journal of Future Generation Communication and Networking 11, no. 1 (January 31, 2018): 1–12. http://dx.doi.org/10.14257/ijfgcn.2018.11.1.01.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Vaziri Yazdi, Seyed Abdolazim, and Mahdieh Ghazvini. "Countermeasure with Primary User Emulation Attack in Cognitive Radio Networks." Wireless Personal Communications 108, no. 4 (May 21, 2019): 2261–77. http://dx.doi.org/10.1007/s11277-019-06521-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Mirza, Muhammad Ayzed, Mudassar Ahmad, Muhammad Asif Habib, Nasir Mahmood, C. M. Nadeem Faisal, and Usman Ahmad. "CDCSS: cluster-based distributed cooperative spectrum sensing model against primary user emulation (PUE) cyber attacks." Journal of Supercomputing 74, no. 10 (April 17, 2018): 5082–98. http://dx.doi.org/10.1007/s11227-018-2378-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Avila, Jayapalan, and Karrupasamy Thenmozhi. "Authentication scheme to combat a primary user emulation attack against cognitive radio users." Security and Communication Networks 8, no. 18 (October 2, 2015): 4242–53. http://dx.doi.org/10.1002/sec.1339.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Ammar, Mahmoud A., and Salahedin A. Rehan. "Cognitive Radio Networks Based on Users' Reputation Scheme." AL-MUKHTAR JOURNAL OF SCIENCES 36, no. 2 (June 30, 2021): 129–34. http://dx.doi.org/10.54172/mjsc.v36i2.35.

Full text

Abstract:

Cognitive Radio (CR) can be defined as a technology that allows users to change the transmission parameters as required to increase the spectrum efficiency. Because of this mechanism, some threats emerge. Two major threats are found in CR. The first is the Primary User Emulation Attack (PUEA), where the attacker is able to transmit at a forbidden time slot effectively, emulating the signals of the primary user. This makes all the system users believe that the spectrum is occupied by a good primary user. The second threat is known as the spectrum sensing data falsification attack (SSDF). In this case, the attackers send false observation information, intentionally or unintentionally, to the fusion center (FC), causing it to make the wrong decision. In this study, the scheme presented was based on a users' reputation for secure spectrum access in cognitive radio networks. Each Secondary User (SU) performs local sensing and then forwards the sensing results to the main fusion center FC. The FC makes the final decision about the presence of the primary user based on the proposed approach. The schemes substantially reduce the effect of cognitive users with low reputation values while improving the impact of cognitive users with the high reputation values on the final decision. It has been verified that the proposed approach can improve the sensing performance under the impact of a different number of reliable and unreliable users in a CR network. Results based on simulation show that the proposed scheme outperforms the traditional majority scheme despite a high number of malicious users.

APA, Harvard, Vancouver, ISO, and other styles

32

Nguyen Thanh, Nhan, Philippe Ciblat, Anh T. Pham, and Van-Tam Nguyen. "Surveillance Strategies Against Primary User Emulation Attack in Cognitive Radio Networks." IEEE Transactions on Wireless Communications 14, no. 9 (September 2015): 4981–93. http://dx.doi.org/10.1109/twc.2015.2430865.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Yadav, Kuldeep, Binod Prasad, Abhijit Bhowmick, Sanjay Dhar Roy, and Sumit Kundu. "Throughput performance under primary user emulation attack in cognitive radio networks." International Journal of Communication Systems 30, no. 18 (July 20, 2017): e3371. http://dx.doi.org/10.1002/dac.3371.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Chin, Wen Long, Trong Nghia Le, Chun Lin Tseng, Wei Che Kao, Chun Shen Tsai, and Chun Wei Kao. "Cooperative detection of primary user emulation attacks based on channel-tap power in mobile cognitive radio networks." International Journal of Ad Hoc and Ubiquitous Computing 15, no. 4 (2014): 263. http://dx.doi.org/10.1504/ijahuc.2014.061005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Li, Husheng, and Zhu Han. "Dogfight in Spectrum: Combating Primary User Emulation Attacks in Cognitive Radio Systems, Part I: Known Channel Statistics." IEEE Transactions on Wireless Communications 9, no. 11 (November 2010): 3566–77. http://dx.doi.org/10.1109/twc.2010.091510.100629.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Li, Husheng, and Zhu Han. "Dogfight in Spectrum: Combating Primary User Emulation Attacks in Cognitive Radio Systems—Part II: Unknown Channel Statistics." IEEE Transactions on Wireless Communications 10, no. 1 (January 2011): 274–83. http://dx.doi.org/10.1109/twc.2010.112310.100630.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Ghauri, Sabir, Chuang Yang, Rui Wang, and Hui Sun. "Analysis of an approach to reducing drops of secondary user on primary user emulation attack." International Journal of Computer Applications in Technology 61, no. 4 (2019): 253. http://dx.doi.org/10.1504/ijcat.2019.10024876.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Sun, Hui, Chuang Yang, Rui Wang, and Sabir Ghauri. "Analysis of an approach to reducing drops of secondary user on primary user emulation attack." International Journal of Computer Applications in Technology 61, no. 4 (2019): 253. http://dx.doi.org/10.1504/ijcat.2019.103302.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Sharifi, Abbas Ali, Morteza Sharifi, and Mir Javad Musevi Niya. "Collaborative Spectrum Sensing under Primary User Emulation Attack in Cognitive Radio Networks." IETE Journal of Research 62, no. 2 (September 28, 2015): 205–11. http://dx.doi.org/10.1080/03772063.2015.1083907.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Karimi, Mohammad, and Seyed Mohammad Sajad Sadough. "Efficient Transmission Strategy for Cognitive Radio Systems Under Primary User Emulation Attack." IEEE Systems Journal 12, no. 4 (December 2018): 3767–74. http://dx.doi.org/10.1109/jsyst.2017.2747594.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Kakalou, Ioanna, and Kostas E. Psannis. "Coordination Without Collaboration in Imperfect Games: The Primary User Emulation Attack Example." IEEE Access 6 (2018): 5402–14. http://dx.doi.org/10.1109/access.2018.2791519.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Rehman, Aasia, and Deo Prakash. "Detection of PUE Attack in CRN with Reduced Error in Location Estimation Using Novel Bat Algorithm." International Journal of Wireless Networks and Broadband Technologies 6, no. 2 (July 2017): 1–25. http://dx.doi.org/10.4018/ijwnbt.2017070101.

Full text

Abstract:

Cognitive Radio Network Technology makes the efficient utilization of scarce spectrum resources by allowing the unlicensed users to opportunistically use the licensed spectrum. Cognitive Radio Network due to its flexible and open nature is vulnerable to a number of security attacks. This paper is mainly concerned with one of the physical layer attack called Primary User Emulation Attack and its detection. This paper solves the problem of PUE attack by localization technique based on TDOA measurements with reduced error in location estimation using a Novel Bat Algorithm (NBA). A number of cooperative secondary users are used for detecting the PUEA by comparing its estimated position with the known position of incumbent. The main goal of NBA is to minimize two fitness functions namely non-linear least square and the maximum likelihood in order to optimize the estimation error. After evaluation, simulation results clearly demonstrates that NBA results in reduced estimation error as compared to Taylor Series Estimation and Particle Swarm Optimization.

APA, Harvard, Vancouver, ISO, and other styles

43

Armi, N., W. Gharibi, and W. Z. Khan. "Error rate detection due to primary user emulation attack in cognitive radio networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 5385. http://dx.doi.org/10.11591/ijece.v10i5.pp5385-5391.

Full text

Abstract:

Security threat is a crucial issue in cognitive radio network (CRN). These threats come from physical layer, data link layer, network layer, transport layer, and application layer. Hence, security system to all layers in CRN has a responsibility to protect the communication between among Secondary User (SU) or to maintain valid detection to the presence of Primary User (PU) signals. Primary User Emulation Attack (PUEA) is a threat on physical layer where malicious user emulates PU signal. This paper studies the effect of exclusive region of PUEA in CRN. We take two setting of exclusive distances, 30m and 50m, where this radius of area is free of malicious users. Probability of false alarm (Pf) and miss detection (Pm) are used to evaluate the performances. The result shows that increasing distance of exclusive region may decrease Pf and Pm.

APA, Harvard, Vancouver, ISO, and other styles

44

Haghighat, M., and S. M. S. Sadough. "Smart primary user emulation in cognitive radio networks: defence strategies against radio-aware attacks and robust spectrum sensing." Transactions on Emerging Telecommunications Technologies 26, no. 9 (August 12, 2014): 1154–64. http://dx.doi.org/10.1002/ett.2848.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Elghamrawy, Sally M. "Security in Cognitive Radio Network: Defense against Primary User Emulation attacks using Genetic Artificial Bee Colony (GABC) algorithm." Future Generation Computer Systems 109 (August 2020): 479–87. http://dx.doi.org/10.1016/j.future.2018.08.022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Arun, S., and G. Umamaheswari. "Cross Layer Design Based Channel Aware Multipath Routing Towards Primary User Emulation Attack." Journal of Computational and Theoretical Nanoscience 14, no. 11 (November 1, 2017): 5209–14. http://dx.doi.org/10.1166/jctn.2017.7131.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Tan, Y., S. Sengupta, and K. P. Subbalakshmi. "Primary user emulation attack in dynamic spectrum access networks: a game-theoretic approach." IET Communications 6, no. 8 (2012): 964. http://dx.doi.org/10.1049/iet-com.2010.0573.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Avila, J., and K. Thenmozhi. "Error Control Code Based Resistance against Primary User Emulation Attack in Cognitive Radio." Asian Journal of Scientific Research 8, no. 3 (June 15, 2015): 324–32. http://dx.doi.org/10.3923/ajsr.2015.324.332.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

CHATTERJEE, PINAKI SANKAR, and MONIDEEPA ROY. "Detecting PUE Attack by Measuring Aberrational Node Behavior in CWSN." Journal of Interconnection Networks 18, no. 01 (March 2018): 1850004. http://dx.doi.org/10.1142/s0219265918500044.

Full text

Abstract:

Primary User Emulation (PUE) attack is a type of Denial of Service (DoS) attack in Cognitive Wireless Sensor Network (CWSN), where malicious secondary users (SU) try to emulate primary users (PU) to maximize their own spectrum usage or obstruct other SU from accessing the spectrum. In this paper, we have designed an application to monitor the SU’s behavior with respect to the CWSN normal behavior profile towards it’s one hop neighbor. Abnormal behavior towards PUE attack of any SU helps us to identify PUE attackers in the network. Our application does not require extensive computational capabilities and memory and therefore suitable for resource constraint cognitive sensor nodes.

APA, Harvard, Vancouver, ISO, and other styles

50

Muñoz, Ernesto Cadena, Luis Fernando Pedraza, and Cesar Augusto Hernández. "Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks." Sensors 22, no. 13 (June 21, 2022): 4659. http://dx.doi.org/10.3390/s22134659.

Full text

Abstract:

Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!