RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers

Hüseyin Parmaksız; Cihan Karakuzu

doi:10.35377/saucis...1973654

RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers

Abstract

The increasing popularity of Internet of Things (IoT) devices has made them vulnerable to attackers, making customized security measures difficult due to limited resources. To address this issue, RF fingerprints, features of the physical layer of wireless signals, are being used to identify and authenticate IoT devices. This study uses low-cost software-defined radio (SDR), a single-board computer, and open-source software to capture and record RF signals. The captured signals are analyzed using the Hilbert transform to determine features for classification. Various statistical methods are applied to select the most relevant features, with the Minimum Redundancy and Maximum Relevance technique further reducing feature size. For the first time in RF fingerprinting, ELM-based Meta-ELM, Meta Multilayer ELM (Meta-MLELM), and Meta Constrained Mixed ELM (Meta-CMELM) algorithms are used as classifiers for device identification. The performance of these classifiers is evaluated based on the RF fingerprint dataset, which consist of 3752 raw signals from 7 devices. The Meta-MLELM classifier has an accuracy rate of 88%, while the Meta-ELM classifier has an accuracy rate of around 90%. The Meta-CMELM classifier performs with an accuracy rate of 92%.

Keywords

Project Number

2021-01.BSEU.01-01

Ethical Statement

The data used in this study consist solely of RF signals collected from commercially available IoT devices in a controlled laboratory environment. No personal, biometric, or confidential information was collected or processed. The dataset does not contain any human subject data, and therefore, no informed consent was required.

Thanks

This research is funded by Bilecik S¸eyh Edebali University Scientific Research Projects under project number 2021- 01.BS¸EU¨ .01-01. We gratefully acknowledge the Turkish Science e-Infrastructure (TRUBA) for providing access to high-performance multi-core processors and computational resources.

References

F. T. Al-Dhief et al., “A survey of voice pathology surveillance systems based on Internet of Things and machine learning algorithms,” IEEE Access, vol. 8, pp. 64514–64533, 2020, doi:10.1109/ACCESS.2020.2984925.
S. Chen et al., “Radio frequency fingerprint-based intelligent mobile edge computing for Internet of Things authentication,” Sensors, vol. 19, no. 16, Art. no. 3610, 2019, doi:10.3390/s19163610.
K. Zhang, S. Leng, Y. He, S. Maharjan, and Y. Zhang, “Mobile edge computing and networking for green and low-latency Internet of Things,” IEEE Commun. Mag., vol. 56, no. 5, pp. 39–45, 2018, doi:10.1109/MCOM.2018.1700882.
M. A. Muhal, X. Luo, Z. Mahmood, and A. Ullah, “Physical unclonable function based authentication scheme for smart devices in Internet of Things,” in Proc. IEEE Int. Conf. Smart Internet of Things (SmartIoT), 2018, pp. 160–165, doi:10.1109/SmartIoT.2018.00037.
P. Angueira et al., “A survey of physical layer techniques for secure wireless communications in industry,” IEEE Commun. Surveys Tuts., vol. 24, no. 2, pp. 810–838, 2022, doi:10.1109/COMST.2022.3148857.
T.-Y. Lin, C.-M. Lai, and C.-W. Chen, “Using SDR platform to extract the RF fingerprint of the wireless devices for device identification,” in Proc. 10th Int. Conf. Comput. Sci., Eng. Appl. (CCSEA 2020), Computer Science & Information Technology, vol. 10, pp. 161–174, 2020, doi:10.5121/csit.2020.101013.
R. Akeela and B. Dezfouli, “Software-defined radios: Architecture, state-of-the-art, and challenges,” Comput. Commun., vol. 128, pp. 106–125, 2018, doi:10.1016/j.comcom.2018.07.012.
M. B. Sruthi, M. Abirami, A. Manikkoth, R. Gandhiraj, and K. P. Soman, “Low cost digital transceiver design for software defined radio using RTL-SDR,” in Proc. Int. Multi-Conf. Autom., Comput., Commun., Control Compressed Sens. (iMac4s), 2013, pp. 852–855, doi:10.1109/iMac4s.2013.6526525.

B. Paillassa and C. Morlet, “Flexible satellites: Software radio in the sky,” in Proc. 10th Int. Conf. Telecommun. (ICT), vol. 2, 2003, pp. 1596–1600, doi:10.1109/ICTEL.2003.1191672.
W. Xiang, F. Sotiropoulos, and S. Liu, “xRadio: A novel software-defined radio (SDR) platform and its exemplar application to vehicle-to-vehicle communications,” in Proc. 14th Int. Conf. Ad-Hoc, Mobile, Wireless Netw. (ADHOC-NOW), Athens, Greece, 2015, pp. 404–415, doi:10.1007/978-3-319- 19662-6_28.
J. Seo et al., “A real-time capable software-defined receiver using GPU for adaptive anti-jam GPS sensors,” Sensors, vol. 11, no. 9, pp. 8966–8991, 2011, doi:10.3390/s110908966.
Y. Chen, S. Lu, H.-S. Kim, D. Blaauw, R. G. Dreslinski, and T. Mudge, “A low power software-defined-radio baseband processor for the Internet of Things,” in Proc. IEEE Int. Symp. High Perform. Comput. Archit. (HPCA), 2016, pp. 40–51, doi:10.1109/HPCA.2016.7446052.
Y. Park, S. Kuk, I. Kang, and H. Kim, “Overcoming IoT language barriers using smartphone SDRs,” IEEE Trans. Mobile Comput., vol. 16, no. 3, pp. 816–828, 2017, doi:10.1109/TMC.2016.2570749.
H. Parmaksız and C. Karakuzu, “A review of recent developments on secure authentication using RF fingerprints techniques,” Sakarya Univ. J. Comput. Inf. Sci., vol. 5, no. 3, pp. 278–303, 2022, doi:10.35377/saucis...1084024.
M. K¨ose, S. Tas¸cıo˘glu, and Z. Telatar, “RF fingerprinting of IoT devices based on transient energy spectrum,” IEEE Access, vol. 7, pp. 18715–18726, 2019, doi:10.1109/ACCESS.2019.2896696.
M. Klaseen and Y.-H. Pao, “The functional link net in structural pattern recognition,” in Proc. IEEE Region 10 Conf. Comput. Commun. Syst. (TENCON), vol. 2, 1990, pp. 567–571, doi:10.1109/TENCON.1990.152674.
D. Husmeier, “Random vector functional link (RVFL) networks,” in Neural Networks for Conditional Probability Estimation: Forecasting Beyond Point Predictions. London, U.K.: Springer, 1999, pp. 87–97, doi:10.1007/978-1-4471-0847-4_6.
Y. Ren, P. N. Suganthan, N. Srikanth, and G. Amaratunga, “Random vector functional link network for short-term electricity load demand forecasting,” Inf. Sci., vol. 367–368, pp. 1078–1093, 2016, doi:10.1016/j.ins.2015.11.039.
Q. Shi, R. Katuwal, P. N. Suganthan, and M. Tanveer, “Random vector functional link neural network based ensemble deep learning,” Pattern Recognit., vol. 117, Art. no. 107978, 2021, doi:10.1016/j.patcog.2021.107978.
G.-B. Huang, L. Chen, and C.-K. Siew, “Universal approximation using incremental constructive feedforward networks with random hidden nodes,” IEEE Trans. Neural Netw., vol. 17, no. 4, pp. 879–892, 2006, doi:10.1109/TNN.2006.875977.
C. Karakuzu, “Performance comparison of a neural network and a fuzzy network trained by ELM for dynamic system identification problems,” presented at the 2nd Int. Congr. Eng. Archit. (ENAR), Marmaris, T¨urkiye, Apr. 22–24, 2019.
G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 2, pp. 513–529, 2012, doi:10.1109/TSMCB.2011.2168604.
G. Huang, S. Song, J. N. D. Gupta, and C. Wu, “Semi-supervised and unsupervised extreme learning machines,” IEEE Trans. Cybern., vol. 44, no. 12, pp. 2405–2417, 2014, doi:10.1109/TCYB.2014.2307349.
G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006, doi:10.1016/j.neucom.2005.12.126.
W. Zhu, J. Miao, and L. Qing, “Constrained extreme learning machines: A study on classification cases,” 2015, arXiv:1501.06115.
M. Billah and S. Waheed, “Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection,” Multimedia Tools Appl., vol. 79, no. 33–34, pp. 23633–23643, 2020, doi:10.1007/s11042-020-09151-7.
V. Brik, S. Banerjee, M. Gruteser, and S. Oh, “Wireless device identification with radiometric signatures,” in Proc. 14th ACM Int. Conf. Mobile Comput. Netw. (MobiCom), San Francisco, CA, USA, 2008, pp. 116–127, doi:10.1145/1409944.1409959.
R. W. Klein, M. A. Temple, and M. J. Mendenhall, “Application of wavelet-based RF fingerprinting to enhance wireless network security,” J. Commun. Netw., vol. 11, no. 6, pp. 544–555, 2009, doi:10.1109/JCN.2009.6388408.
B. Danev, T. S. Heydt-Benjamin, and S. Capkun, “Physical-layer identification of RFID devices,” in Proc. 18th USENIX Secur. Symp., Montreal, QC, Canada, 2009, pp. 199–214.
B. Danev and S. Capkun, “Transient-based identification of wireless sensor nodes,” in Proc. Int. Conf. Inf. Process. Sensor Netw. (IPSN), San Francisco, CA, USA, 2009, pp. 25–36.
W. E. Cobb, E. W. Garcia, M. A. Temple, R. O. Baldwin, and Y. C. Kim, “Physical layer identification of embedded devices using RF-DNA fingerprinting,” in Proc. IEEE Mil. Commun. Conf. (MILCOM), 2010, pp. 2168–2173, doi:10.1109/MILCOM.2010.5680487.
N. T. Nguyen, G. Zheng, Z. Han, and R. Zheng, “Device fingerprinting to enhance wireless security using nonparametric Bayesian method,” in Proc. IEEE INFOCOM, 2011, pp. 1404–1412, doi:10.1109/INFCOM.2011.5934926.
C. Bertoncini, K. Rudd, B. Nousain, and M. Hinders, “Wavelet fingerprinting of radio-frequency identification (RFID) tags,” IEEE Trans. Ind. Electron., vol. 59, no. 12, pp. 4843–4850, 2012, doi:10.1109/TIE.2011.2179276.
S. U. Rehman, K. Sowerby, and C. Coghill, “RF fingerprint extraction from the energy envelope of an instantaneous transient signal,” in Proc. Aust. Commun. Theory Workshop (AusCTW), 2012, pp. 90–95, doi:10.1109/AusCTW.2012.6164912.
Y. Yuan, Z. Huang, H. Wu, and X. Wang, “Specific emitter identification based on Hilbert–Huang transform-based time–frequency–energy distribution features,” IET Commun., vol. 8, no. 13, pp. 2404–2412, 2014, doi:10.1049/iet-com.2013.0865.
J.-H. Liang, Z.-T. Huang, and Z.-W. Li, “Method of empirical mode decomposition in specific emitter identification,” Wireless Pers. Commun., vol. 96, no. 2, pp. 2447–2461, 2017, doi:10.1007/s11277-017-4306-0.
L. Ding, S. Wang, F. Wang, and W. Zhang, “Specific emitter identification via convolutional neural networks,” IEEE Commun. Lett., vol. 22, no. 12, pp. 2591–2594, 2018, doi:10.1109/LCOMM.2018.2871465.
L. Peng, J. Zhang, M. Liu, and A. Hu, “Deep learning based RF fingerprint identification using differential constellation trace figure,” IEEE Trans. Veh. Technol., vol. 69, no. 1, pp. 1091–1095, 2020, doi:10.1109/TVT.2019.2950670.
K. Sankhe, M. Belgiovine, F. Zhou, S. Riyaz, S. Ioannidis, and K. Chowdhury, “ORACLE: Optimized radio classification through convolutional neural networks,” in Proc. IEEE INFOCOM, 2019, pp. 370–378, doi:10.1109/INFOCOM.2019.8737463.
M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, “Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference,” IEEE Open J. Commun. Soc., vol. 1, pp. 60–76, 2020, doi:10.1109/OJCOMS.2019.2955889.
A. Al-Shawabka et al., “Exposing the fingerprint: Dissecting the impact of the wireless channel on radio fingerprinting,” in Proc. IEEE INFOCOM, 2020, pp. 646–655, doi:10.1109/INFOCOM41043.2020.9155259.
N. Soltani, G. Reus-Muns, B. Salehi, J. Dy, S. Ioannidis, and K. Chowdhury, “RF fingerprinting unmanned aerial vehicles with non-standard transmitter waveforms,” IEEE Trans. Veh. Technol., vol. 69, no. 12, pp. 15518–15531, 2020, doi:10.1109/TVT.2020.3042128.
L. Chen, C. Zhao, Y. Zheng, and Y. Wang, “Radio frequency fingerprint identification based on transfer learning,” in Proc. IEEE/CIC Int. Conf. Commun. China (ICCC), 2021, pp. 81–85, doi:10.1109/ICCC52777.2021.9580203.
J. Zhang, Z. Xu, J. Li, Q. Dai, Z. Ling, and M. Yang, “Wi-Fi device identification based on multi-domain physical layer fingerprint,” Comput. Commun., vol. 204, pp. 118–129, 2023, doi:10.1016/j.comcom.2023.03.024.
M. K¨ose, S. Tas¸cıo˘glu, and Z. Telatar, “Wireless device identification using descriptive statistics,” Commun. Fac. Sci. Univ. Ankara, Ser. A2-A3 Phys. Sci. Eng., vol. 57, no. 1, pp. 1–10, 2015, doi:10.1501/commua1-2_0000000083.
G. Baldini and G. Steri, “A survey of techniques for the identification of mobile phones using the physical fingerprints of the built-in components,” IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1761–1789, 2017, doi:10.1109/COMST.2017.2694487.
B. Danev, D. Zanetti, and S. Capkun, “On physical-layer identification of wireless devices,” ACM Comput. Surv., vol. 45, no. 1, Nov. 2012, Art. no. 6, pp. 1–29, doi:10.1145/2379776.2379782.
O. Ureten and N. Serinken, “Wireless security through RF fingerprinting,” Can. J. Elect. Comput. Eng., vol. 32, no. 1, pp. 27–33, 2007, doi:10.1109/CJECE.2007.364330.
M. Abirami, V. Hariharan, M. B. Sruthi, R. Gandhiraj, and K. P. Soman, “Exploiting GNU radio and USRP: An economical test bed for real-time communication systems,” in Proc. 4th Int. Conf. Comput., Commun. Netw. Technol. (ICCCNT), 2013, pp. 1–6, doi:10.1109/ICCCNT.2013.6726630.
R. W. Woodings and M. Gerrior, “Avoiding interference in the 2.4-GHz ISM band,” EE Times, Jul. 1, 2006. [Online]. Available: https://www.eetimes.com/ avoiding-interference-in-the-2-4-ghz-ism-band/
B. Bloessl, M. Segata, C. Sommer, and F. Dressler, “An IEEE 802.11a/g/p OFDM receiver for GNU radio,” in Proc. 2nd Workshop Softw. Radio Implement. Forum (SRIF), 2013, pp. 9–16, doi:10.1145/2491246.2491248.
H. Parmaksız and C. Karakuzu, “Yazılım tanımlı radyoya dayalı RF parmak izi toplamak ic¸in d¨us¸¨uk maliyetli bir c¸¨oz¨um,” (in Turkish), Rahva J. Tech. Soc. Stud., vol. 2, no. 2, pp. 179–188, 2022. [Online]. Available: https://izlik.org/JA37YE39YN
P. Y. Ktonas and N. Papp, “Instantaneous envelope and phase extraction from real signals: Theory, implementation, and an application to EEG analysis,” Signal Process., vol. 2, no. 4, pp. 373–385, 1980, doi:10.1016/0165-1684(80)90079-1.
B. Picinbono, “On instantaneous amplitude and phase of signals,” IEEE Trans. Signal Process., vol. 45, no. 3, pp. 552–560, 1997, doi:10.1109/78.558469.
S. Abbas et al., “Improving security of the Internet of Things via RF fingerprinting based device identification system,” Neural Comput. Appl., vol. 33, no. 21, pp. 14753–14769, 2021, doi:10.1007/s00521-021-06115-2.
M. A. A. Albadr, M. Ayob, S. Tiun, F. T. Al-Dhief, A. Arram, and S. Khalaf, “Breast cancer diagnosis using the fast learning network algorithm,” Front. Oncol., vol. 13, Art. no. 1150840, 2023, doi:10.3389/fonc.2023.1150840.
B. Raju, N. Verma, G. Narendra, O. Silakari, and B. Sapra, “Classification of potent and weak penetration enhancers using multiple feature selection methods and machine learning models,” J. Pharm. Innov., vol. 18, no. 4, pp. 1778–1797, 2023, doi:10.1007/s12247-023-09757-y.
P. Scanlon, I. O. Kennedy, and Y. Liu, “Feature extraction approaches to RF fingerprinting for device identification in femtocells,” Bell Labs Tech. J., vol. 15, no. 3, pp. 141–151, 2010, doi:10.1002/bltj.20462.
C. Dubendorfer, B. Ramsey, and M. Temple, “ZigBee device verification for securing industrial control and building automation systems,” in Critical Infrastructure Protection VII (ICCIP 2013), IFIP Advances in Information and Communication Technology. Berlin, Germany: Springer, 2013, pp. 47–62, doi:10.1007/978-3-642-45330-4_4.
H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226–1238, 2005, doi:10.1109/TPAMI.2005.159.
E. Uzundurukan, Y. Dalveren, and A. Kara, “A database for the radio frequency fingerprinting of Bluetooth devices,” Data, vol. 5, no. 2, Art. no. 55, 2020, doi:10.3390/data5020055.
M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, 2020, “Drone remote controller RF signal dataset,” IEEE DataPort, doi:10.21227/ss99- 8d56.
Y. Liu, J. Wang, S. Niu, and H. Song, 2021, “ADS-B signals records for non-cryptographic identification and incremental learning,” IEEE Dataport, doi:10.21227/1bxc-ke87.
Y. Liu, J. Wang, H. Song, S. Niu, and T. Yang, 2020, “A 24-hour signal recording dataset with labels for cybersecurity and IoT,” IEEE Dataport, doi:10.21227/gt9v-kz32.
A. Jagannath, J. Jagannath, and P. S. P. V. Kumar, “A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges,” Comput. Netw., vol. 219, Art. no. 109455, 2022, doi:10.1016/j.comnet.2022.109455.
A. Al-Shawabka, F. Restuccia, S. D’Oro, and T. Melodia, “Massive-scale I/Q datasets for WiFi radio fingerprinting,” Comput. Netw., vol. 182, Art. no. 107566, 2020, doi:10.1016/j.comnet.2020.107566.
C. R. Rao and S. K. Mitra, “Generalized inverse of a matrix and its applications,” in Proc. 6th Berkeley Symp. Math. Statist. Probab., vol. 1. Berkeley, CA, USA: Univ. of California Press, 1972, pp. 601–620.
S. Liao and C. Feng, “Meta-ELM: ELM with ELM hidden nodes,” Neurocomputing, vol. 128, pp. 81–87, 2014, doi:10.1016/j.neucom.2013.01.060.
H. Parmaksız and C. Karakuzu, “Performance analysis of extreme learning machine classifiers on radio frequency fingerprinting,” BSEU J. Eng. Res. Technol., vol. 3, no. 2, pp. 1–7, 2022. [Online]. Available: https://bseujert.bilecik.edu.tr/index.php/bseujert/issue/view/5/23
Y. Lan, Y. C. Soh, and G.-B. Huang, “A constructive enhancement for online sequential extreme learning machine,” in Proc. Int. Joint Conf. Neural Netw. (IJCNN), 2009, pp. 1708–1713, doi:10.1109/IJCNN.2009.5178608.
Q.-Y. Zhu, A. K. Qin, P. N. Suganthan, and G.-B. Huang, “Evolutionary extreme learning machine,” Pattern Recognit., vol. 38, no. 10, pp. 1759–1763, 2005, doi:10.1016/j.patcog.2005.03.028.
H.-J. Rong, Y.-S. Ong, A.-H. Tan, and Z. Zhu, “A fast pruned-extreme learning machine for classification problem,” Neurocomputing, vol. 72, no. 1–3, pp. 359–366, 2008, doi:10.1016/j.neucom.2008.01.005.
D. Yu and L. Deng, “Efficient and effective algorithms for training single-hidden-layer neural networks,” Pattern Recognit. Lett., vol. 33, no. 5, pp. 554–558, 2012, doi:10.1016/j.patrec.2011.12.002.
J. Tang, C. Deng, and G.-B. Huang, “Extreme learning machine for multilayer perceptron,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 4, pp. 809–821, 2016, doi:10.1109/TNNLS.2015.2424995.
H. Zhou, G.-B. Huang, Z. Lin, H. Wang, and Y. C. Soh, “Stacked extreme learning machines,” IEEE Trans. Cybern., vol. 45, no. 9, pp. 2013–2025, 2015, doi:10.1109/TCYB.2014.2363492.
G. Atac Kale and C. Karakuzu, “Multilayer extreme learning machines and their modeling performance on dynamical systems,” Appl. Soft Comput., vol. 122, Art. no. 108861, 2022, doi:10.1016/j.asoc.2022.108861.
L. L. C. Kasun, H. Zhou, G.-B. Huang, and C. M. Vong, “Representational learning with ELMs for big data,” IEEE Intell. Syst., vol. 28, no. 6, pp. 31–34, 2013.
R. Kaur, R. K. Roul, and S. Batra, “Multilayer extreme learning machine: A systematic review,” Multimedia Tools Appl., vol. 82, no. 26, pp. 40269–40307, 2023, doi:10.1007/s11042-023-14634-4.
N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate online sequential learning algorithm for feedforward networks,” IEEE Trans. Neural Netw., vol. 17, no. 6, pp. 1411–1423, 2006, doi:10.1109/TNN.2006.880583.
G.-B. Huang, X. Ding, and H. Zhou, “Optimization method based extreme learning machine for classification,” Neurocomputing, vol. 74, no. 1–3, pp. 155–163, 2010, doi:10.1016/j.neucom.2010.02.019.
Q. Liu, Q. He, and Z. Shi, “Extreme support vector machine classifier,” in Advances in Knowledge Discovery and Data Mining (PAKDD 2008), Lecture Notes in Computer Science. Berlin, Germany: Springer, 2008, pp. 222–233, doi:10.1007/978-3-540-68125-0_21.

Details

Primary Language

English

Subjects

System and Network Security, Software and Application Security, Applied Computing (Other)

Journal Section

Research Article

Authors

Hüseyin Parmaksız ^*
0000-0001-8455-5625
Türkiye

Cihan Karakuzu
0000-0003-0569-098X
Türkiye

Early Pub Date

October 27, 2026

Publication Date

October 30, 2026

Submission Date

June 18, 2026

Acceptance Date

July 22, 2026

Published in Issue

Year 2026 Volume: 9 Number: 5

DOI

https://doi.org/10.35377/saucis...1973654

IZ

https://izlik.org/JA44RY54TN

Cite

RIS / Bibtex

APA

Parmaksız, H., & Karakuzu, C. (2026). RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers. Sakarya University Journal of Computer and Information Sciences, 9(5), 1464-1484. https://doi.org/10.35377/saucis...1973654

AMA

1.Parmaksız H, Karakuzu C. RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers. SAUCIS. 2026;9(5):1464-1484. doi:10.35377/saucis.1973654

Chicago

Parmaksız, Hüseyin, and Cihan Karakuzu. 2026. “RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers”. Sakarya University Journal of Computer and Information Sciences 9 (5): 1464-84. https://doi.org/10.35377/saucis. 1973654.

EndNote

Parmaksız H, Karakuzu C (October 1, 2026) RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers. Sakarya University Journal of Computer and Information Sciences 9 5 1464–1484.

IEEE

[1]H. Parmaksız and C. Karakuzu, “RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers”, SAUCIS, vol. 9, no. 5, pp. 1464–1484, Oct. 2026, doi: 10.35377/saucis...1973654.

ISNAD

Parmaksız, Hüseyin - Karakuzu, Cihan. “RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers”. Sakarya University Journal of Computer and Information Sciences 9/5 (October 1, 2026): 1464-1484. https://doi.org/10.35377/saucis. 1973654.

JAMA

1.Parmaksız H, Karakuzu C. RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers. SAUCIS. 2026;9:1464–1484.

MLA

Parmaksız, Hüseyin, and Cihan Karakuzu. “RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 5, Oct. 2026, pp. 1464-8, doi:10.35377/saucis. 1973654.

Vancouver

1.Hüseyin Parmaksız, Cihan Karakuzu. RF Fingerprint-Based Identification of IoT Devices Using Meta-ELM Classifiers. SAUCIS. 2026 Oct. 1;9(5):1464-8. doi:10.35377/saucis. 1973654