{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:22:59Z","timestamp":1760149379924,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jinan \u201c20 New Colleges and Universities\u201d Introduction and Innovation Team","award":["2021GXRC064"],"award-info":[{"award-number":["2021GXRC064"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Utilizing a multi-frame signal (MFS) rather than a single-frame signal (SFS) for radio frequency fingerprint authentication (RFFA) shows the advantage of higher accuracy. However, previous studies have often overlooked the associated security threats in MFS-based RFFA. In this paper, we focus on the carrier-sense multiple access with collision avoidance channel and identify a potential security threat, in that an attacker may inject a forged frame into valid traffic, making it more likely to be accepted alongside legitimate frames. To counter such a security threat, we propose an innovative design called the inter-frame-relationship protected signal (IfrPS), which enables the receiver to determine whether two consecutively received frames originate from the same transmitter to safeguard the MFS-based RFFA. To demonstrate the applicability of our proposition, we analyze and numerically evaluate two important properties: its impact on message demodulation and the accuracy gain in IfrPS-aided, MFS-based RFFA compared with the SFS-based RFFA. Our results show that the proposed scheme has a minimal impact of only \u22120.5 dB on message demodulation, while achieving up to 5 dB gain for RFFA accuracy.<\/jats:p>","DOI":"10.3390\/s23156948","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T09:28:29Z","timestamp":1691141309000},"page":"6948","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Inter-Frame-Relationship Protected Signal: A New Design for Radio Frequency Fingerprint Authentication"],"prefix":"10.3390","volume":"23","author":[{"given":"Xufei","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7009-2586","authenticated-orcid":false,"given":"Shuiguang","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Topal, O.A., and Kurt, G.K. (2022, January 10\u201313). Physical Layer Authentication for LEO Satellite Constellations. Proceedings of the 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA.","DOI":"10.1109\/WCNC51071.2022.9771727"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6154","DOI":"10.1109\/TVT.2022.3231633","article-title":"Physical Layer Authentication based on Integrated Semi-Supervised Learning in Wireless Networks for Dynamic Industrial Scenarios","volume":"72","author":"Du","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_3","first-page":"1","article-title":"Analysis and Compensation of Transmitter and Receiver I\/Q Imbalances in Space-Time Coded Multiantenna OFDM Systems","volume":"2008","author":"Zou","year":"2008","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liao, R., Wen, H., Wu, J., Pan, F., Xu, A., Jiang, Y., Xie, F., and Cao, M. (2019). Deep-learning-based physical layer authentication for industrial wireless sensor networks. Sensors, 19.","DOI":"10.3390\/s19112440"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102780","DOI":"10.1016\/j.adhoc.2022.102780","article-title":"Visibility Graph Entropy based Radiometric Feature for Physical Layer Identification","volume":"127","author":"Zeng","year":"2022","journal-title":"Hoc. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, Y., Zhu, J., Zeng, S., Shen, Y., Jiang, X., and Zhang, D. (2023). Fractal Dimension of DSSS Frame Preamble: Radiometric Feature for Wireless Device Identification. IEEE Trans. Mob. Comput., 1\u201315.","DOI":"10.1109\/TMC.2023.3235497"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Brik, V., Banerjee, S., Gruteser, M., and Oh, S. (2008, January 14\u201319). Wireless device identification with radiometric signatures. Proceedings of the 14th ACM International Conference on Mobile Computing and Networking, New York, NY, USA.","DOI":"10.1145\/1409944.1409959"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pan, F., Wen, H., Liao, R., Jiang, Y., Xu, A., Ouyang, K., and Zhu, X. (2017, January 9\u201311). Physical layer authentication based on channel information and machine learning. Proceedings of the 2017 IEEE Conference on Communications and Network Security (CNS), Las Vegas, NV, USA.","DOI":"10.1109\/CNS.2017.8228660"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bari, M.F., Chatterjee, B., and Sen, S. (2021, January 22\u201328). Dirac: Dynamic-irregular clustering algorithm with incremental learning for rf-based trust augmentation in iot device authentication. Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Virtual.","DOI":"10.1109\/ISCAS51556.2021.9401403"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1109\/JIOT.2021.3086581","article-title":"On Physical-Layer Authentication Via Online Transfer Learning","volume":"9","author":"Chen","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yamazaki, T., Iwagami, S., and Miyoshi, T. (2019, January 18\u201320). Ant-inspired Backoff-based Opportunistic Routing for Ad Hoc Networks. Proceedings of the 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), Matsue, Japan.","DOI":"10.23919\/APNOMS.2019.8893049"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1109\/JSAC.2011.110812","article-title":"Identifying wireless users via transmitter imperfections","volume":"29","author":"Polak","year":"2011","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/TCCN.2020.3024610","article-title":"Contour stella image and deep learning for signal recognition in the physical layer","volume":"7","author":"Lin","year":"2020","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1109\/TIFS.2021.3056206","article-title":"Reinforcement Learning-based Physical-layer Authentication for Controller Area Networks","volume":"16","author":"Xiao","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TCCN.2019.2949308","article-title":"No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-layer Hardware Impairments","volume":"6","author":"Sankhe","year":"2019","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Al-Shawabka, A., Restuccia, F., D\u2019Oro, S., Jian, T., Rendon, B.C., Soltani, N., Dy, J., Ioannidis, S., Chowdhury, K., and Melodia, T. (2020, January 6\u20139). Exposing the Fingerprint: Dissecting the Impact of the Wireless Channel on Radio Fingerprinting. Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications, Toronto, ON, Canada.","DOI":"10.1109\/INFOCOM41043.2020.9155259"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1109\/JIOT.2019.2960099","article-title":"Multiuser Physical Layer Authentication in Internet of Things with Data Augmentation","volume":"7","author":"Liao","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100306","DOI":"10.1016\/j.iot.2020.100306","article-title":"A ZigBee intrusion detection system for IoT using secure and efficient data collection","volume":"12","author":"Fal","year":"2020","journal-title":"Internet Things"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"22596","DOI":"10.1109\/TITS.2022.3146024","article-title":"NovelADS: A Novel Anomaly Detection System for Intra-Vehicular Networks","volume":"23","author":"Agrawal","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","unstructured":"Bellardo, J., and Savage, S. (2003, January 4\u20138). 802.11 Denial-of-Service Attacks: Real Vulnerabilities and Practical Solutions. Proceedings of the 12th Conference on USENIX Security Symposium, Washington, DC, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, X., Xu, W., Wang, S., Li, Y., and Lin, Z. (2022, January 10\u201313). An Anomaly Detection Scheme with K-means aided Extended Isolation Forest in RSS-based Wireless Positioning System. Proceedings of the 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA.","DOI":"10.1109\/WCNC51071.2022.9771602"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, Z., Ying, J., Shen, J., Feng, Y., Chen, Q.A., Mao, Z.M., and Liu, H.X. (2023). Anomaly Detection Against GPS Spoofing Attacks on Connected and Autonomous Vehicles Using Learning From Demonstration. IEEE Trans. Intell. Transp. Syst., 1\u201314.","DOI":"10.1109\/TITS.2023.3269029"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/JSAC.2023.3273769","article-title":"On the Latent Space of mmWave MIMO Channels for NLOS Identification in 5G-Advanced Systems","volume":"41","author":"Tedeschini","year":"2023","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16110","DOI":"10.1109\/TVT.2020.3042431","article-title":"Certificateless and Lightweight Authentication Scheme for Vehicular Communication Networks","volume":"69","author":"Hathal","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/IOTM.001.2200024","article-title":"Beyond Traditional Message Authentication Codes: Future Solutions for Efficient Authentication of Message Streams in IoT Networks","volume":"5","author":"Bansal","year":"2022","journal-title":"IEEE Internet Things Mag."},{"key":"ref_26","unstructured":"(2011). IEEE Standard for Local and Metropolitan Area Networks\u2014Part 15.4: Low-Rate Wireless Personal Area Networks (LR-WPANs). Revision of IEEE Std 802.15.4-2006 (Standard No. IEEE Std 802.15.4-2011)."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1109\/LSP.2017.2710144","article-title":"Watermark BER and Channel Capacity Analysis for QPSK-Based RF Watermarking by Constellation Dithering in AWGN Channel","volume":"24","author":"Xu","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2710","DOI":"10.1109\/JLT.2022.3142347","article-title":"Novel phase and CFO estimation DSP for photonics-based sub-THz communication","volume":"40","author":"Young","year":"2022","journal-title":"J. Light. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, X., Zeng, S., and Tong, W. (2018, January 12\u201315). Enhancing Carrier Frequency Offset Authentication via Fractal Dimension. Proceedings of the 2018 International Conference on Networking and Network Applications (NaNA), Xi\u2019an, China.","DOI":"10.1109\/NANA.2018.8648734"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6948\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:25:54Z","timestamp":1760127954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6948"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":29,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23156948"],"URL":"https:\/\/doi.org\/10.3390\/s23156948","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,8,4]]}}}