{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T22:57:30Z","timestamp":1774047450289,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T00:00:00Z","timestamp":1568764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T00:00:00Z","timestamp":1568764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["11471053"],"award-info":[{"award-number":["11471053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Laboratory of University Wireless Communications","award":["2015103"],"award-info":[{"award-number":["2015103"]}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["3182028"],"award-info":[{"award-number":["3182028"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Wireless Com Network"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n              <jats:p>For wireless transmission, radio-frequency device anti-cloning has become a major security issue. Radio-frequency distinct native attribute (RF-DNA) fingerprint is a developing technology to find the difference among RF devices and identify them. Comparing with previous research, (1) this paper proposed that mean (<jats:italic>\u03bc<\/jats:italic>) feature should be added into RF-DNA fingerprint. Thus, totally four statistics (mean, standard deviation, skewness, and kurtosis) were calculated on instantaneous amplitude, phase, and frequency generated by Hilbert transform. (2) We first proposed using the logistic regression (LR) and support vector machine (SVM) to recognize such extracted fingerprint at different signal-to-noise ratio (SNR) environment. We compared their performance with traditional multiple discriminant analysis (MDA). (3) In addition, this paper also proposed to extract three sub-features (amplitude, phase, and frequency) separately to recognize extracted fingerprint under MDA. In order to make our results more universal, additive white Gaussian noise was adopted to simulate the real environment. The results show that (1) mean feature conducts an improvement in the classification accuracy, especially in low SNR environment. (2) MDA and SVM could successfully identify these RF devices, and the classification accuracy could reach 94%. Although the classification accuracy of LR is 89.2%, it could get the probability of each class. After adding a different noise, the recognition accuracy is more than 80% when <jats:italic>SNR<\/jats:italic>\u22655 dB using MDA or SVM. (3) Frequency feature has more discriminant information. Phase and amplitude play an auxiliary but also pivotal role in classification recognition.<\/jats:p>","DOI":"10.1186\/s13638-019-1544-8","type":"journal-article","created":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T14:08:22Z","timestamp":1568815702000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Identification and authentication for wireless transmission security based on RF-DNA fingerprint"],"prefix":"10.1186","volume":"2019","author":[{"given":"Xueli","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yufeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hongxin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaofeng","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Guangyuan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,18]]},"reference":[{"key":"1544_CR1","doi-asserted-by":"publisher","unstructured":"D. R. Reising, M. A. Temple, M. J. Mendenhall, in Wireless Communications and NETWORKING Conference. Improving intra-cellular security using air monitoring with RF fingerprints (IEEE, 2010), pp. 1\u20136. \n                    https:\/\/doi.org\/10.1109\/wcnc.2010.5506229\n                    \n                  .","DOI":"10.1109\/wcnc.2010.5506229"},{"issue":"1","key":"1544_CR2","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/TIFS.2011.2160170","volume":"7","author":"W. E. Cobb","year":"2012","unstructured":"W. E. Cobb, E. D. Laspe, R. O. Baldwin, M. A. Temple, C. K. Yong, Intrinsic physical-layer authentication of integrated circuits. IEEE Trans. Inf. Forensics Secur.7(1), 14\u201324 (2012).","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1544_CR3","unstructured":"J. Hall, M. Barbeau, E. Kranakis, in IASTED International Multi-Conference on Wireless and Optical Communications. Detection of transient in radio frequency fingerprinting using signal phase, (2003)."},{"issue":"1","key":"1544_CR4","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/COMST.2015.2476338","volume":"18","author":"Q. Xu","year":"2016","unstructured":"Q. Xu, R. Zheng, W. Saad, Z. Han, Device fingerprinting in wireless networks: challenges and opportunities. IEEE Commun. Surv. Tutor.18(1), 94\u2013104 (2016).","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"1544_CR5","doi-asserted-by":"publisher","unstructured":"S. U. Rehman, K. W. Sowerby, S. Alam, I. Ardekani, in Communications and Network Security. Radio frequency fingerprinting and its challenges (IEEE, 2014), pp. 496\u2013497. \n                    https:\/\/doi.org\/10.1109\/cns.2014.6997522\n                    \n                  .","DOI":"10.1109\/cns.2014.6997522"},{"key":"1544_CR6","unstructured":"C. K. Dubendorfer, B. W. Ramsey, M. A. Temple, in Military Communications Conference, 2012 - Milcom. An RF-DNA verification process for ZigBee networks, (2013), pp. 1\u20136."},{"issue":"8","key":"1544_CR7","doi-asserted-by":"publisher","first-page":"1862","DOI":"10.1109\/TIFS.2016.2561902","volume":"11","author":"T. J. Bihl","year":"2017","unstructured":"T. J. Bihl, K. W. Bauer, M. A. Temple, Feature selection for RF fingerprinting with multiple discriminant analysis and using ZigBee device emissions. IEEE Trans. Inf. Forensic Secur.11(8), 1862\u20131874 (2017).","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"1544_CR8","doi-asserted-by":"publisher","unstructured":"W. E. Cobb, E. W. Garcia, M. A. Temple, R. O. Baldwin, in Military Communications Conference, 2010 - Milcom. Physical layer identification of embedded devices using RF-DNA fingerprinting (IEEE, 2010), pp. 2168\u20132173. \n                    https:\/\/doi.org\/10.1109\/milcom.2010.5680487\n                    \n                  .","DOI":"10.1109\/milcom.2010.5680487"},{"key":"1544_CR9","doi-asserted-by":"publisher","unstructured":"M. D. Williams, M. A. Temple, D. R. Reising, in Global Telecommunications Conference. Augmenting bit-level network security using physical layer RF-DNA fingerprinting (IEEE, 2010), pp. 1\u20136. \n                    https:\/\/doi.org\/10.1109\/glocom.2010.5683789\n                    \n                  .","DOI":"10.1109\/glocom.2010.5683789"},{"key":"1544_CR10","doi-asserted-by":"publisher","unstructured":"D. R. Reising, M. A. Temple, in IEEE International Conference on Communications. Wimax mobile subscriber verification using Gabor-based RF-DNA fingerprints (IEEE, 2012), pp. 1005\u20131010. \n                    https:\/\/doi.org\/10.1109\/icc.2012.6364039\n                    \n                  .","DOI":"10.1109\/icc.2012.6364039"},{"issue":"6","key":"1544_CR11","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1109\/TIFS.2015.2400426","volume":"10","author":"D. R. Reising","year":"2015","unstructured":"D. R. Reising, M. A. Temple, J. A. Jackson, Authorized and rogue device discrimination using dimensionally reduced RF-DNA fingerprints. IEEE Trans. Inf. Forensic Secur.10(6), 1180\u20131192 (2015).","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"issue":"1","key":"1544_CR12","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TR.2014.2372432","volume":"64","author":"H. J. Patel","year":"2015","unstructured":"H. J. Patel, M. A. Temple, R. O. Baldwin, Improving ZigBee device network authentication using ensemble decision tree classifiers with radio frequency distinct native attribute fingerprinting. IEEE Trans. Reliab.64(1), 221\u2013233 (2015).","journal-title":"IEEE Trans. Reliab."},{"issue":"2","key":"1544_CR13","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/S0304-3800(99)00113-1","volume":"120","author":"S. Manel","year":"1999","unstructured":"S. Manel, J. M. Dias, S. J. Ormerod, Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a himalayan river bird. Ecol. Model.120(2), 337\u2013347 (1999).","journal-title":"Ecol. Model."},{"key":"1544_CR14","doi-asserted-by":"publisher","unstructured":"P. K. Harmer, D. R. Reising, M. A. Temple, in IEEE International Conference on Communications. Classifier selection for physical layer security augmentation in cognitive radio networks (IEEE, 2013), pp. 2846\u20132851. \n                    https:\/\/doi.org\/10.1109\/icc.2013.6654972\n                    \n                  .","DOI":"10.1109\/icc.2013.6654972"},{"issue":"4","key":"1544_CR15","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1007\/s00034-015-0108-3","volume":"35","author":"H. Li","year":"2016","unstructured":"H. Li, H. Liang, L. Cao, L. Cao, X. Feng, C. Tang, E. Li, Novel ecg signal classification based on kica nonlinear feature extraction. Circ. Syst. Signal Process.35(4), 1187\u20131197 (2016).","journal-title":"Circ. Syst. Signal Process."},{"issue":"1","key":"1544_CR16","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/CJECE.2007.364330","volume":"32","author":"O. Ureten","year":"2007","unstructured":"O. Ureten, N. Serinken, Wireless security through rf fingerprinting. Can. J. Electr. Comput. Eng.32(1), 27\u201333 (2007).","journal-title":"Can. J. Electr. Comput. Eng."},{"issue":"5","key":"1544_CR17","first-page":"398","volume":"17","author":"B. Abedi","year":"2017","unstructured":"B. Abedi, A. Abbasi, A. Goshvarpour, Investigating the effect of traditional persian music on ecg signals in young women using wavelet transform and neural networks. Anatolia J. Cardiol.17(5), 398\u2013403 (2017).","journal-title":"Anatolia J. Cardiol."},{"issue":"10","key":"1544_CR18","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1049\/el.2015.0051","volume":"51","author":"M. Lukacs","year":"2015","unstructured":"M. Lukacs, P. Collins, M. Temple, Classification performance using \u2019RF-DNA\u2019 fingerprinting of ultra-wideband noise waveforms. Electron. Lett.51(10), 787\u2013789 (2015).","journal-title":"Electron. Lett."},{"issue":"8","key":"1544_CR19","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/S0893-6080(02)00079-5","volume":"15","author":"B. Hammer","year":"2002","unstructured":"B. Hammer, T. Villmann, Generalized relevance learning vector quantization. Neural Netw.15(8), 1059\u20131068 (2002).","journal-title":"Neural Netw."},{"key":"1544_CR20","doi-asserted-by":"publisher","unstructured":"N. Hu, Y. D. Yao, in IEEE International Conference on Communications. Identification of legacy radios in a cognitive radio network using a radio frequency fingerprinting based method (IEEE, 2012), pp. 1597\u20131602. \n                    https:\/\/doi.org\/10.1109\/icc.2012.6364436\n                    \n                  .","DOI":"10.1109\/icc.2012.6364436"},{"issue":"3","key":"1544_CR21","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1002\/bltj.20462","volume":"15","author":"P. Scanlon","year":"2010","unstructured":"P. Scanlon, I. O. Kennedy, Y. Liu, Feature extraction approaches to RF fingerprinting for device identification in femtocells. Bell Labs Tech. J.15(3), 141\u2013151 (2010).","journal-title":"Bell Labs Tech. J."},{"key":"1544_CR22","doi-asserted-by":"publisher","unstructured":"S. Chen, F. Xie, Y. Chen, H. Song, H. Wen, in IEEE International Symposium on Electromagnetic Compatibility. Identification of wireless transceiver devices using radio frequency (RF) fingerprinting based on STFT analysis to enhance authentication security (IEEE, 2017), pp. 1\u20135. \n                    https:\/\/doi.org\/10.1109\/emc-b.2017.8260381\n                    \n                  .","DOI":"10.1109\/emc-b.2017.8260381"},{"issue":"6","key":"1544_CR23","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1109\/JCN.2009.6388408","volume":"11","author":"R. W. Klein","year":"2012","unstructured":"R. W. Klein, M. A. Temple, M. J. Mendenhall, Application of wavelet-based RF fingerprinting to enhance wireless network security. J. Commun. Netw.11(6), 544\u2013555 (2012).","journal-title":"J. Commun. Netw."},{"key":"1544_CR24","doi-asserted-by":"publisher","unstructured":"M. K. D. Williams, S. A. Munns, M. A. Temple, M. J. Mendenhall, in International Conference on Network and System Security. RF-DNA fingerprinting for airport WiMax communications security (IEEE, 2010), pp. 32\u201339. \n                    https:\/\/doi.org\/10.1109\/nss.2010.21\n                    \n                  .","DOI":"10.1109\/nss.2010.21"},{"key":"1544_CR25","doi-asserted-by":"publisher","unstructured":"C. Zhao, L. Huang, L. Hu, Y. Yao, in International Conference on Computer Science & Education. Transient fingerprint feature extraction for WLAN cards based on polynomial fitting (IEEE, 2011), pp. 1099\u20131102. \n                    https:\/\/doi.org\/10.1109\/iccse.2011.6028826\n                    \n                  .","DOI":"10.1109\/iccse.2011.6028826"},{"key":"1544_CR26","unstructured":"J. Hall, M. Barbeau, E. Kranakis, in IASTED International Multi-Conference on Wireless and Optical Communications. Detection of transient in radio frequency fingerprinting using signal phase, (2003)."},{"key":"1544_CR27","doi-asserted-by":"publisher","unstructured":"T. Debnath, M. M. Hasan, T. Biswas, in International Conference on Electrical and Computer Engineering. Analysis of ECG signal and classification of heart abnormalities using artificial neural network (IEEE, 2017), pp. 353\u2013356. \n                    https:\/\/doi.org\/10.1109\/icece.2016.7853929\n                    \n                  .","DOI":"10.1109\/icece.2016.7853929"},{"key":"1544_CR28","doi-asserted-by":"publisher","unstructured":"M. Cheng, W. J. Sori, F. Jiang, A. Khan, S. Liu, in IEEE International Conference on Computational Science and Engineering. Recurrent neural network based classification of ECG signal features for obstruction of sleep apnea detection (IEEE, 2017), pp. 199\u2013202. \n                    https:\/\/doi.org\/10.1109\/cse-euc.2017.220\n                    \n                  .","DOI":"10.1109\/cse-euc.2017.220"}],"container-title":["EURASIP Journal on Wireless Communications and Networking"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-019-1544-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13638-019-1544-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-019-1544-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T12:33:43Z","timestamp":1600346023000},"score":1,"resource":{"primary":{"URL":"https:\/\/jwcn-eurasipjournals.springeropen.com\/articles\/10.1186\/s13638-019-1544-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,18]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["1544"],"URL":"https:\/\/doi.org\/10.1186\/s13638-019-1544-8","relation":{},"ISSN":["1687-1499"],"issn-type":[{"value":"1687-1499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,18]]},"assertion":[{"value":"6 September 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"230"}}