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Wireless Personal Communications
Published in: Wireless Personal Communications 3/2023

21-03-2023

Extreme Learning Machine Based Identification of Malicious Users for Secure Cooperative Spectrum Sensing in Cognitive Radio Networks

Authors: Manish Kumar Giri, Saikat Majumder

Published in: Wireless Personal Communications | Issue 3/2023

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Abstract

Cognitive radio (CR) technology has evolved over the traditional radio to successfully utilize the unused frequency spectrum. In CR the secondary users (SUs) perform cooperative spectrum sensing to access the available frequency band. The opportunistic nature of sensing prevents any interference with primary users (PUs) in the network. However, the presence of security threats like malicious users (MUs) strongly influences the performance. In CR network, MUs act like normal SUs and transmit false information to the fusion center and degrades the performance. To overcome this issue, we proposed an extreme learning machine (ELM) based approach to classify the legitimate SUs with the MUs. In this work, ELM is used as a classifier to separate the legitimate SUs and MUs. Extensive simulation results are presented to highlight the effectiveness of the proposed approach. The proposed approach highlights significant improvement in terms of training time and provides better trade-off compare to the other competitive techniques in the literature.
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Literature
1.
Federal Communications Commission. (2003). Notice of proposed rule making and order. ET Docket No. 03-222
2.
Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.CrossRef
3.
Zhai, L., Wang, H., & Gao, C. (2016). A spectrum access based on quality of service (QoS) in cognitive radio networks. PloS one, 11(5), e0155074.CrossRef
4.
Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116–130.CrossRef
5.
Axell, E., Leus, G., Larsson, E. G., & Poor, H. V. (2012). Spectrum sensing for cognitive radio: State-of-the-art and recent advances. IEEE Signal Processing Magazine, 29(3), 101–116.CrossRef
6.
Khan, M. S., Jibran, M., Koo, I., Kim, S. M., & Kim, J. (2019). A double adaptive approach to tackle malicious users in cognitive radio networks. Wireless Communications and Mobile Computing, 2019, 2350694.CrossRef
7.
Mishra, S. M., Sahai, A., & Brodersen, R. W. (2006). Cooperative sensing among cognitive radios. In 2006 IEEE international conference on communications (vol. 4, pp. 1658–1663). IEEE.
8.
He, Y., Xue, J., Ratnarajah, T., Sellathurai, M., & Khan, F. (2016). On the performance of cooperative spectrum sensing in random cognitive radio networks. IEEE Systems Journal, 12(1), 881–892.CrossRef
9.
Chilakala, S., & Ram, M. S. S. (2018). Optimization of cooperative secondary users in cognitive radio networks. Engineering Science and Technology, An International Journal, 21(5), 815–821.CrossRef
10.
Jenani, M. (2017). Network security, a challenge. International Journal of Advanced Networking and Applications, 8(5), 120–123.
11.
Marinho, J., Granjal, J., & Monteiro, E. (2015). A survey on security attacks and countermeasures with primary user detection in cognitive radio networks. EURASIP Journal on Information Security, 1, 1–14.
12.
Wu, H., Sun, X., Guo, C., & Ren, S. (2016). Malicious user detection for wide-band cognitive radio networks. In 2016 Asia-Pacific microwave conference (APMC) (pp. 1–4). IEEE.
13.
Taggu, A., Chunka, C., & Marchang, N. (2015). Codes: A collaborative detection strategy for ssdf attacks in cognitive radio networks. In Proceedings of the third international symposium on women in computing and informatics (pp. 118–123).
14.
Sarala, B., Devi, S. R., Suganthy, M., & Ida, S. J. (2019). A novel authentication mechanism for cognitive radio networks. International Journal of Recent Technology and Engineering, 8(4), 713–718.
15.
Wan, R., Ding, L., Xiong, N., & Zhou, X. (2019). Mitigation strategy against spectrum-sensing data falsification attack in cognitive radio sensor networks. International Journal of Distributed Sensor Networks, 15(9), 1550147719870645.CrossRef
16.
Farmani, F., Abbasi-Jannatabad, M., & Berangi, R. (2011). Detection of SSDF attack using SVDD algorithm in cognitive radio networks. In 2011 third international conference on computational intelligence, communication systems and networks (pp. 201–204). IEEE.
17.
Kaligineedi, P., Khabbazian, M., & Bhargava, V. K. (2010). Malicious user detection in a cognitive radio cooperative sensing system. IEEE Transactions on Wireless Communications, 9(8), 2488–2497.CrossRef
18.
He, X., Dai, H., & Ning, P. (2013). Hmm-based malicious user detection for robust collaborative spectrum sensing. IEEE Journal on Selected Areas in Communications, 31(11), 2196–2208.CrossRef
19.
Li, J., Liu, J., & Long, K. (2010). Reliable cooperative spectrum sensing algorithm based on Dempster–Shafer theory. In 2010 IEEE Global Telecommunications Conference GLOBECOM 2010 (pp. 1–5). IEEE.
20.
Sharifi, A. A., & Mofarreh-Bonab, M. (2018). Spectrum sensing data falsification attack in cognitive radio networks: An analytical model for evaluation and mitigation of performance degradation. AUT Journal of Electrical Engineering, 50(1), 43–50.
21.
Gul, N., Qureshi, I. M., Elahi, A., & Rasool, I. (2018). Defense against malicious users in cooperative spectrum sensing using genetic algorithm. International Journal of Antennas and Propagation, 2018, 2346317.CrossRef
22.
Dave, M. B., & Nakrani, M. B. (2014). Malicious user detection in spectrum sensing for wran using different outliers detection techniques. arXiv preprint arXiv:​1405.​2685
23.
Adelantado, F., & Verikoukis, C. (2013). Detection of malicious users in cognitive radio ad hoc networks: A non-parametric statistical approach. Ad Hoc Networks, 11(8), 2367–2380.CrossRef
24.
Elangovan, K., Krishnasamy Tamilselvam, Y., Mohan, R. E., Iwase, M., Nemoto, T., & Wood, K. (2017). Fault diagnosis of a reconfigurable crawling-rolling robot based on support vector machines. Applied Sciences, 7(10), 1025.CrossRef
25.
Jan, S. U., Lee, Y. D., Shin, J., & Koo, I. (2017). Sensor fault classification based on support vector machine and statistical time-domain features. IEEE Access, 5, 8682–8690.CrossRef
26.
Wang, F., Zhen, Z., Wang, B., & Mi, Z. (2017). Comparative study on KNN and SVM based weather classification models for day ahead short term solar PV power forecasting. Applied Sciences, 8(1), 28.CrossRef
27.
Shah, H. A., & Koo, I. (2018). Reliable machine learning based spectrum sensing in cognitive radio networks. In Wireless Communications and Mobile Computing 2018.
28.
Zhu, J., Song, Y., Jiang, D., & Song, H. (2017). A new deep-q-learning-based transmission scheduling mechanism for the cognitive internet of things. IEEE Internet of Things Journal, 5(4), 2375–2385.CrossRef
29.
Giri, M. K., & Majumder, S. (2021). Eigenvalue-based cooperative spectrum sensing using kernel fuzzy c-means clustering. Digital Signal Processing, 111, 102996.CrossRef
30.
Giri, M. K., & Majumder, S. (2022). On eigenvalue-based cooperative spectrum sensing using feature extraction and maximum entropy fuzzy clustering. Journal of Ambient Intelligence and Humanized Computing, 1–15.
31.
Huang, Y. D., Liang, Y. C., & Yang, G. (2016). A fuzzy support vector machine algorithm for cooperative spectrum sensing with noise uncertainty. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6). IEEE.
32.
Jan, S. U., Vu, V. H., & Koo, I. (2018). Throughput maximization using an SVM for multi-class hypothesis-based spectrum sensing in cognitive radio. Applied Sciences, 8(3), 421.CrossRef
33.
Li, Z., Wu, W., Liu, X., & Qi, P. (2018). Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks. IET Communications, 12(19), 2485–2492.CrossRef
34.
Rahman, M., Lee, Y. D., Koo, I., et al. (2016). An efficient transmission mode selection based on reinforcement learning for cooperative cognitive radio networks. Human-centric Computing and Information Sciences, 6(1), 1–14.CrossRef
35.
Alshawaqfeh, M., Wang, X., Ekti, A. R., Shakir, M. Z., Qaraqe, K., & Serpedin, E. (2015). A survey of machine learning algorithms and their applications in cognitive radio. In International conference on cognitive radio oriented wireless networks (pp. 790–801). Springer.
36.
37.
Cadena Muñoz, E., Pedraza Martínez, L. F., & Ortiz Triviño, J. E. (2020). Detection of malicious primary user emulation based on a support vector machine for a mobile cognitive radio network using software-defined radio. Electronics, 9(8), 1282.CrossRef
38.
Chen, C., Song, M., Xin, C., & Alam, M. (2012). A robust malicious user detection scheme in cooperative spectrum sensing. In 2012 IEEE global communications conference (GLOBECOM) (pp. 4856–4861). IEEE.
39.
Albehadili, A., Ali, A., Jahan, F., Javaid, A. Y., Oluochy, J., & Devabhaktuniz, V. (2019). Machine learning-based primary user emulation attack detection in cognitive radio networks using pattern described link-signature (PDLS). In 2019 wireless telecommunications symposium (WTS) (pp. 1–7). IEEE.
40.
Lu, J., Li, L., Chen, G., Shen, D., Pham, K., & Blasch, E. (2017). Machine learning based intelligent cognitive network using fog computing. Sensors and Systems for Space Applications X, SPIE, 10196, 149–157.
41.
Furqan, H. M., Aygül, M. A., & Nazzal, M. (2020). Primary user emulation and jamming attack detection in cognitive radio via sparse coding. EURASIP Journal on Wireless Communications and Networking, 1, 1–19.
42.
Khan, M. S., Khan, L., Gul, N., Amir, M., Kim, J., & Kim, S. M. (2020). Support vector machine-based classification of malicious users in cognitive radio networks. Wireless Communications and Mobile Computing, 2020, 8846948.CrossRef
43.
Hossain, M. S., & Miah, M. S. (2021). Machine learning-based malicious user detection for reliable cooperative radio spectrum sensing in cognitive radio-internet of things. Machine Learning with Applications, 5, 100052.CrossRef
44.
Zhang, Y., Wu, Q., & Shikh-Bahaei, M. R. (2020). On ensemble learning-based secure fusion strategy for robust cooperative sensing in full-duplex cognitive radio networks. IEEE Transactions on Communications, 68(10), 6086–6100.CrossRef
45.
Kumar, G. P., & Reddy, D. K. (2022). Hierarchical cat and mouse based ensemble extreme learning machine for spectrum sensing data falsification attack detection in cognitive radio network. Microprocessors and Microsystems, 90, 104523.CrossRef
46.
Xu, Z., Sun, Z., & Guo, L. (2021). Throughput maximization of collaborative spectrum sensing under SSDF attacks. IEEE Transactions on Vehicular Technology, 70(8), 8378–8383.CrossRef
47.
Fu, Y., & He, Z. (2019). Bayesian-inference-based sliding window trust model against probabilistic SSDF attack in cognitive radio networks. IEEE Systems Journal, 14(2), 1764–1775.CrossRef
48.
Ponnusamy, V., Kottursamy, K., Karthick, T., Mukeshkrishnan, M., Malathi, D., & Ahanger, T. A. (2020). Primary user emulation attack mitigation using neural network. Computers & Electrical Engineering, 88, 106849.CrossRef
49.
Taggu, A., & Marchang, N. (2021). Detecting byzantine attacks in cognitive radio networks: a two-layered approach using hidden Markov model and machine learning. Pervasive and Mobile Computing, 77, 101461.CrossRef
50.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501.CrossRef
51.
Hansen, L. K., & Salamon, P. (1990). Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10), 993–1001.CrossRef
52.
Huang, G. B., Wang, D. H., & Lan, Y. (2011). Extreme learning machines: A survey. International Journal of Machine Learning and Cybernetics, 2(2), 107–122.CrossRef
53.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. In 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541) (Vol. 2, pp. 985-990). IEEE.
54.
55.
Giri, M. K., & Majumder, S. (2022). Cooperative spectrum sensing using extreme learning machines for cognitive radio networks. IETE Technical Review, 39, 698–712.CrossRef
Metadata
Title
Extreme Learning Machine Based Identification of Malicious Users for Secure Cooperative Spectrum Sensing in Cognitive Radio Networks
Authors
Manish Kumar Giri
Saikat Majumder
Publication date
21-03-2023
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 3/2023
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-023-10368-6

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