{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T19:15:16Z","timestamp":1770059716614,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Electronics and Telecommunications Research Institute (ETRI)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat of unconventional drone and swarm attacks. In order to deal with threats, drones need to be classified by intercepting their Radio Frequency (RF) signals. With the arrival of Sixth Generation (6G) networks, it is required to develop sophisticated methods to properly categorize drone signals in order to achieve optimal resource sharing, high-security levels, and mobility management. However, deep ensemble learning has not been investigated properly in the case of 6G. It is anticipated that it will incorporate drone-based BTS and cellular networks that, in one way or another, may be subjected to jamming, intentional interferences, or other dangers from unauthorized UAVs. Thus, this study is conducted based on Radio Frequency Fingerprinting (RFF) of drones identified to detect unauthorized ones so that proper actions can be taken to protect the network\u2019s security and integrity. This paper proposes a novel method\u2014a Composite Ensemble Learning (CEL)-based neural network\u2014for drone signal classification. The proposed method integrates wavelet-based denoising and combines automatic and manual feature extraction techniques to foster feature diversity, robustness, and performance enhancement. Through extensive experiments conducted on open-source benchmark datasets of drones, our approach demonstrates superior classification accuracies compared to recent benchmark deep learning techniques across various Signal-to-Noise Ratios (SNRs). This novel approach holds promise for enhancing communication efficiency, security, and safety in 6G networks amidst the proliferation of drone-based applications.<\/jats:p>","DOI":"10.3390\/s24175618","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T07:45:47Z","timestamp":1725003947000},"page":"5618","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Composite Ensemble Learning Framework for Passive Drone Radio Frequency Fingerprinting in Sixth-Generation Networks"],"prefix":"10.3390","volume":"24","author":[{"given":"Muhammad Usama","family":"Zahid","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering Department, Sir Syed CASE Institute of Technology, Islamabad 04524, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Danish","family":"Nisar","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Sir Syed CASE Institute of Technology, Islamabad 04524, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2526-8436","authenticated-orcid":false,"given":"Adnan","family":"Fazil","sequence":"additional","affiliation":[{"name":"Department of Avionics Engineering, Air University, E-9, Islamabad 44230, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihyoung","family":"Ryu","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Research Institute (ETRI), Gwangju 61012, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maqsood Hussain","family":"Shah","sequence":"additional","affiliation":[{"name":"SFI Insight Centre for Data Analytics and the School of Electronic Engineering, Dublin City University, D09 V209 Dublin, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"ref_1","unstructured":"Radiant Insights (2016). Commercial Drone Market Analysis by Product (Fixed Wing, Rotary Blade, Nano, Hybrid), by Application (Agriculture, Energy, Government, Media & Entertainment) and Segment Forecasts to 2022, Grand View Research."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MCOM.2017.1700452","article-title":"An amateur drone surveillance system based on the cognitive Internet of Things","volume":"56","author":"Ding","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Knott, E.F., Schaeffer, J.F., and Tulley, M.T. (2004). Radar Cross Section, SciTech Publishing.","DOI":"10.1049\/SBRA026E"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1017\/S1759078714000282","article-title":"Classification of small UAVs and birds by micro-Doppler signatures","volume":"6","author":"Molchanov","year":"2014","journal-title":"Int. J. Microw. Wirel. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1017\/aer.2018.158","article-title":"Classification of UAV and bird target in low-altitude airspace with surveillance radar data","volume":"123","author":"Chen","year":"2019","journal-title":"Aeronaut. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Messina, M., and Pinelli, G. (2019, January 3\u201325). Classification of drones with a surveillance radar signal. Proceedings of the 12th International Conference on Computer Vision Systems, Thessaloniki, Greece.","DOI":"10.1007\/978-3-030-34995-0_66"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Samaras, S., Diamantidou, E., Ataloglou, D., Sakellariou, N., Vafeiadis, A., Magoulianitis, V., Lalas, A., Dimou, A., Zarpalas, D., and Votis, K. (2019). Deep learning on multi sensor data for counter UAV applications\u2014A systematic review. Sensors, 19.","DOI":"10.3390\/s19224837"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Saqib, M., Khan, S.D., Sharma, N., and Blumenstein, M. (September, January 29). A study on detecting drones using deep convolutional neural networks. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078541"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1109\/JBHI.2021.3074893","article-title":"COVID-19 automatic diagnosis with radiographic imaging: Explainable attention transfer deep neural networks","volume":"25","author":"Shi","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Devi, D.M.R., Jancy, P.L., Tamilselvi, P., Logeswaran, S., Vigneshwaran, E., and Jeyachandran, A. (2022, January 14\u201316). Detection of Lung Cancer using CNN-ZF NET. Proceedings of the 2022 International Conference on Computer, Power and Communications (ICCPC), Chennai, India.","DOI":"10.1109\/ICCPC55978.2022.10072039"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"23805","DOI":"10.3390\/s150923805","article-title":"Vision-based detection and distance estimation of micro unmanned aerial vehicles","volume":"15","author":"Kalkan","year":"2015","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Diamantidou, E., Lalas, A., Votis, K., and Tzovaras, D. (2021, January 25\u201327). A multimodal AI-leveraged counter-UAV framework for diverse environments. Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations, Hersonissos, Crete, Greece.","DOI":"10.1007\/978-3-030-79157-5_19"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Park, S., Kim, Y., Lee, K., Smith, A.H., Dietz, J.E., and Matson, E.T. (2020). Accessible real-time surveillance radar system for object detection. Sensors, 20.","DOI":"10.3390\/s20082215"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lin, Y., Zha, H., Tu, Y., Zhang, S., Yan, W., and Xu, C. (IEEE Trans. Emerg. Top. Comput. Intell., 2023). GLR-SEI: Green and low resource specific emitter identification based on complex networks and fisher pruning, IEEE Trans. Emerg. Top. Comput. Intell., Early Access.","DOI":"10.1109\/TETCI.2023.3303092"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1109\/TITS.2023.3308716","article-title":"LT-SEI: Long-tailed specific emitter identification based on decoupled representation learning in low-resource scenarios","volume":"25","author":"Zha","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dumitrescu, C., Minea, M., Costea, I.M., Cosmin Chiva, I., and Semenescu, A. (2020). Development of an acoustic system for UAV detection. Sensors, 20.","DOI":"10.3390\/s20174870"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1109\/TVT.2020.2964110","article-title":"An acoustic-based surveillance system for amateur drones detection and localization","volume":"69","author":"Shi","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rahman, M.H., Sejan, M.A.S., Aziz, M.A., Tabassum, R., Baik, J.I., and Song, H.K. (2024). A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions. Remote Sens., 16.","DOI":"10.3390\/rs16050879"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104313","DOI":"10.1016\/j.dib.2019.104313","article-title":"DroneRF dataset: A dataset of drones for RF-based detection, classification and identification","volume":"26","author":"Allahham","year":"2019","journal-title":"Data Brief"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.future.2019.05.007","article-title":"RF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database","volume":"100","author":"Mohamed","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Allahham, M.S., Khattab, T., and Mohamed, A. (2020, January 2\u20135). Deep learning for RF-based drone detection and identification: A multi-channel 1-D convolutional neural networks approach. Proceedings of the 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar.","DOI":"10.1109\/ICIoT48696.2020.9089657"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Al-Emadi, S., and Al-Senaid, F. (2020, January 2\u20135). Drone detection approach based on radio-frequency using convolutional neural network. Proceedings of the 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar.","DOI":"10.1109\/ICIoT48696.2020.9089489"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Akter, R., Doan, V.S., Tunze, G.B., Lee, J.M., and Kim, D.S. (2020, January 21\u201323). RF-based UAV surveillance system: A sequential convolution neural networks approach. Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Republic of Korea.","DOI":"10.1109\/ICTC49870.2020.9289281"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Medaiyese, O.O., Syed, A., and Lauf, A.P. (2021, January 12\u201313). Machine learning framework for RF-based drone detection and identification system. Proceedings of the 2021 2nd International Conference On Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS), Tangerang, Indonesia.","DOI":"10.1109\/ICON-SONICS53103.2021.9617168"},{"key":"ref_25","first-page":"5","article-title":"Support vector machines for classification and regression","volume":"14","author":"Gunn","year":"1998","journal-title":"ISIS Tech. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abunada, A.H., Osman, A.Y., Khandakar, A., Chowdhury, M.E.H., Khattab, T., and Touati, F. (2020, January 2\u20135). Design and implementation of a RF based anti-drone system. Proceedings of the 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar.","DOI":"10.1109\/ICIoT48696.2020.9089515"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nguyen, P., Kakaraparthi, V., Bui, N., Umamahesh, N., Pham, N., Truong, H., Guddeti, Y., Bharadia, D., Han, R., and Frew, E. (2020, January 16\u201319). DroneScale: Drone load estimation via remote passive RF sensing. Proceedings of the 18th Conference on Embedded Networked Sensor Systems, Virtual Event.","DOI":"10.1145\/3384419.3430778"},{"key":"ref_28","first-page":"109","article-title":"Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends","volume":"16","author":"Mohsan","year":"2023","journal-title":"Intell. Serv. Robot."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/OJVT.2022.3142170","article-title":"Satellite-and cache-assisted UAV: A joint cache placement, resource allocation, and trajectory optimization for 6G aerial networks","volume":"3","author":"Tran","year":"2022","journal-title":"IEEE Open J. Veh. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"91119","DOI":"10.1109\/ACCESS.2021.3092039","article-title":"6G enabled unmanned aerial vehicle traffic management: A perspective","volume":"9","author":"Shrestha","year":"2021","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12803","DOI":"10.1109\/ACCESS.2021.3051097","article-title":"Handover management of drones in future mobile networks: 6G technologies","volume":"9","author":"Angjo","year":"2021","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"130860","DOI":"10.1109\/ACCESS.2023.3323399","article-title":"Federated learning meets intelligence reflection surface in drones for enabling 6G networks: Challenges and opportunities","volume":"11","author":"Shvetsov","year":"2023","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rehman, M.U., Akhtar, S., Zakwan, M., and Mahmood, M.H. (2022). Novel architecture with selected feature vector for effective classification of mitotic and non-mitotic cells in breast cancer histology images. Biomed. Signal Process. Control, 71.","DOI":"10.1016\/j.bspc.2021.103212"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6009","DOI":"10.1016\/j.csbj.2021.10.034","article-title":"DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species","volume":"19","author":"Rehman","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"17779","DOI":"10.1109\/ACCESS.2021.3054361","article-title":"m6A-NeuralTool: Convolution neural tool for RNA N6-methyladenosine site identification in different species","volume":"9","author":"Rehman","year":"2021","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1109\/TCBB.2022.3192572","article-title":"DL-m6A: Identification of N6-methyladenosine Sites in Mammals using deep learning based on different encoding schemes","volume":"20","author":"Rehman","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mir, B.A., Rehman, M.U., Tayara, H., and Chong, K.T. (2023). Improving Enhancer Identification with a Multi-Classifier Stacked Ensemble Model. J. Mol. Biol., 435.","DOI":"10.1016\/j.jmb.2023.168314"},{"key":"ref_38","unstructured":"Ezuma, M., Erden, F., Anjinappa, C.K., Ozdemir, O., and Guvenc, I. (2020). Drone Remote Controller RF Signal Dataset, IEEE."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1109\/LCOMM.2021.3084043","article-title":"Radar Emitter Identification Based on Novel Time-Frequency Spectrum and Convolutional Neural Network","volume":"25","author":"Xiao","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"33544","DOI":"10.1109\/ACCESS.2019.2903444","article-title":"Specific Emitter Identification Using Convolutional Neural Network-Based IQ Imbalance Estimators","volume":"7","author":"Wong","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liang, J., Liao, W.H., and Wu, Y.C. (2020, January 3\u20135). Toward Automatic Recognition of Cursive Chinese Calligraphy: An Open Dataset For Cursive Chinese Calligraphy Text. Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), Taichung, Taiwan.","DOI":"10.1109\/IMCOM48794.2020.9001777"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"110055","DOI":"10.1016\/j.asoc.2023.110055","article-title":"Quantum Fruit Fly algorithm and ResNet50-VGG16 for medical diagnosis","volume":"136","author":"Nijaguna","year":"2023","journal-title":"Appl. Soft Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5618\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:45:22Z","timestamp":1760111122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5618"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,29]]},"references-count":42,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24175618"],"URL":"https:\/\/doi.org\/10.3390\/s24175618","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,29]]}}}