Electromagnetic Signal Intelligent Identification Based on Radio Frequency Fingerprints
Due to the open nature of WIFI connection, it is exposing its private information to the attackers. Traditional WIFI security methods are no longer able to meet the current security needs, and more and more wireless-side physical layer security solutions provide solutions, among which RF fingerprint...
Gespeichert in:
| Veröffentlicht in: | Wireless communications and mobile computing 2022-06, Vol.2022, p.1-12 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 12 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | Wireless communications and mobile computing |
| container_volume | 2022 |
| creator | Kang, Jian Mu, Hui Ren, Hui Jia, Jicheng Qi, Lin Zhang, Zherui |
| description | Due to the open nature of WIFI connection, it is exposing its private information to the attackers. Traditional WIFI security methods are no longer able to meet the current security needs, and more and more wireless-side physical layer security solutions provide solutions, among which RF fingerprinting is an endogenous security technology with potential. Constructing an effective and accurate method to identify WIFI devices that steal information is a difficulty that today’s society needs to face. The main problem is not only that the recognition accuracy is difficult to improve but also the problem of data shortage. In this paper, we first construct a large-scale WIFI real-world measurement dataset. Next, we use PSD and bispectrum features, as well as complex ResNet schemes for WIFI device identification experiments, and compare and analyze them from multiple perspectives. The experimental results show that the proposed algorithm can achieve up to 97% recognition accuracy among 100 devices. Moreover, when the SNR is 0 dB, the complex ResNet method can still achieve 78% recognition accuracy among 100 devices. Finally, this paper summarizes the experimental analysis of the measured dataset and discusses the open issues related to this area. |
| doi_str_mv | 10.1155/2022/6296954 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2678218690</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2678218690</sourcerecordid><originalsourceid>FETCH-LOGICAL-c294t-c2a263b3f050a356bc0ae17aa84ff6655fc3bf0f250fe873d4a02caddc4f55003</originalsourceid><addsrcrecordid>eNp9kEtLAzEUhYMoWKs7f8CAS63NY5KZWWpptVAQfOAy3OYxpkwzNUmR_ntTWly6OfcsPi7nHISuCb4nhPMxxZSOBW1Ew8sTNCCc4VEtqur0z4vmHF3EuMIYM0zJAH1OO6NS6NfQepOcKt5c66Er5j6ZrnOt8amY66zOOgXJ9b54hGh0kc0raNcXs2C-t8arXTFzvjVhE5xP8RKdWeiiuTreIfqYTd8nz6PFy9N88rAYKdqUKStQwZbMYo6BcbFUGAypAOrSWiE4t4otLbaUY2vqiukSMFWgtSot57nEEN0c_m5Cn2PEJFf9NuQGUVJR1ZTUotlTdwdKhT7GYKzMKdcQdpJguZ9O7qeTx-kyfnvAv5zX8OP-p38BQ0huvg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2678218690</pqid></control><display><type>article</type><title>Electromagnetic Signal Intelligent Identification Based on Radio Frequency Fingerprints</title><source>Wiley Online Library Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Alma/SFX Local Collection</source><creator>Kang, Jian ; Mu, Hui ; Ren, Hui ; Jia, Jicheng ; Qi, Lin ; Zhang, Zherui</creator><contributor>Liu, Mingqian ; Mingqian Liu</contributor><creatorcontrib>Kang, Jian ; Mu, Hui ; Ren, Hui ; Jia, Jicheng ; Qi, Lin ; Zhang, Zherui ; Liu, Mingqian ; Mingqian Liu</creatorcontrib><description>Due to the open nature of WIFI connection, it is exposing its private information to the attackers. Traditional WIFI security methods are no longer able to meet the current security needs, and more and more wireless-side physical layer security solutions provide solutions, among which RF fingerprinting is an endogenous security technology with potential. Constructing an effective and accurate method to identify WIFI devices that steal information is a difficulty that today’s society needs to face. The main problem is not only that the recognition accuracy is difficult to improve but also the problem of data shortage. In this paper, we first construct a large-scale WIFI real-world measurement dataset. Next, we use PSD and bispectrum features, as well as complex ResNet schemes for WIFI device identification experiments, and compare and analyze them from multiple perspectives. The experimental results show that the proposed algorithm can achieve up to 97% recognition accuracy among 100 devices. Moreover, when the SNR is 0 dB, the complex ResNet method can still achieve 78% recognition accuracy among 100 devices. Finally, this paper summarizes the experimental analysis of the measured dataset and discusses the open issues related to this area.</description><identifier>ISSN: 1530-8669</identifier><identifier>EISSN: 1530-8677</identifier><identifier>DOI: 10.1155/2022/6296954</identifier><language>eng</language><publisher>Oxford: Hindawi</publisher><subject>Algorithms ; Communication ; Communications networks ; Datasets ; Deep learning ; Engineering ; Fault diagnosis ; Fingerprinting ; Identification methods ; Identification systems ; Internet of Things ; Network security ; Radiation ; Security ; Software ; Spectrum analysis ; Wireless networks</subject><ispartof>Wireless communications and mobile computing, 2022-06, Vol.2022, p.1-12</ispartof><rights>Copyright © 2022 Jian Kang et al.</rights><rights>Copyright © 2022 Jian Kang et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c294t-c2a263b3f050a356bc0ae17aa84ff6655fc3bf0f250fe873d4a02caddc4f55003</cites><orcidid>0000-0003-3393-0944</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Liu, Mingqian</contributor><contributor>Mingqian Liu</contributor><creatorcontrib>Kang, Jian</creatorcontrib><creatorcontrib>Mu, Hui</creatorcontrib><creatorcontrib>Ren, Hui</creatorcontrib><creatorcontrib>Jia, Jicheng</creatorcontrib><creatorcontrib>Qi, Lin</creatorcontrib><creatorcontrib>Zhang, Zherui</creatorcontrib><title>Electromagnetic Signal Intelligent Identification Based on Radio Frequency Fingerprints</title><title>Wireless communications and mobile computing</title><description>Due to the open nature of WIFI connection, it is exposing its private information to the attackers. Traditional WIFI security methods are no longer able to meet the current security needs, and more and more wireless-side physical layer security solutions provide solutions, among which RF fingerprinting is an endogenous security technology with potential. Constructing an effective and accurate method to identify WIFI devices that steal information is a difficulty that today’s society needs to face. The main problem is not only that the recognition accuracy is difficult to improve but also the problem of data shortage. In this paper, we first construct a large-scale WIFI real-world measurement dataset. Next, we use PSD and bispectrum features, as well as complex ResNet schemes for WIFI device identification experiments, and compare and analyze them from multiple perspectives. The experimental results show that the proposed algorithm can achieve up to 97% recognition accuracy among 100 devices. Moreover, when the SNR is 0 dB, the complex ResNet method can still achieve 78% recognition accuracy among 100 devices. Finally, this paper summarizes the experimental analysis of the measured dataset and discusses the open issues related to this area.</description><subject>Algorithms</subject><subject>Communication</subject><subject>Communications networks</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Engineering</subject><subject>Fault diagnosis</subject><subject>Fingerprinting</subject><subject>Identification methods</subject><subject>Identification systems</subject><subject>Internet of Things</subject><subject>Network security</subject><subject>Radiation</subject><subject>Security</subject><subject>Software</subject><subject>Spectrum analysis</subject><subject>Wireless networks</subject><issn>1530-8669</issn><issn>1530-8677</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kEtLAzEUhYMoWKs7f8CAS63NY5KZWWpptVAQfOAy3OYxpkwzNUmR_ntTWly6OfcsPi7nHISuCb4nhPMxxZSOBW1Ew8sTNCCc4VEtqur0z4vmHF3EuMIYM0zJAH1OO6NS6NfQepOcKt5c66Er5j6ZrnOt8amY66zOOgXJ9b54hGh0kc0raNcXs2C-t8arXTFzvjVhE5xP8RKdWeiiuTreIfqYTd8nz6PFy9N88rAYKdqUKStQwZbMYo6BcbFUGAypAOrSWiE4t4otLbaUY2vqiukSMFWgtSot57nEEN0c_m5Cn2PEJFf9NuQGUVJR1ZTUotlTdwdKhT7GYKzMKdcQdpJguZ9O7qeTx-kyfnvAv5zX8OP-p38BQ0huvg</recordid><startdate>20220607</startdate><enddate>20220607</enddate><creator>Kang, Jian</creator><creator>Mu, Hui</creator><creator>Ren, Hui</creator><creator>Jia, Jicheng</creator><creator>Qi, Lin</creator><creator>Zhang, Zherui</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7XB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0003-3393-0944</orcidid></search><sort><creationdate>20220607</creationdate><title>Electromagnetic Signal Intelligent Identification Based on Radio Frequency Fingerprints</title><author>Kang, Jian ; Mu, Hui ; Ren, Hui ; Jia, Jicheng ; Qi, Lin ; Zhang, Zherui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c294t-c2a263b3f050a356bc0ae17aa84ff6655fc3bf0f250fe873d4a02caddc4f55003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Communication</topic><topic>Communications networks</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Engineering</topic><topic>Fault diagnosis</topic><topic>Fingerprinting</topic><topic>Identification methods</topic><topic>Identification systems</topic><topic>Internet of Things</topic><topic>Network security</topic><topic>Radiation</topic><topic>Security</topic><topic>Software</topic><topic>Spectrum analysis</topic><topic>Wireless networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kang, Jian</creatorcontrib><creatorcontrib>Mu, Hui</creatorcontrib><creatorcontrib>Ren, Hui</creatorcontrib><creatorcontrib>Jia, Jicheng</creatorcontrib><creatorcontrib>Qi, Lin</creatorcontrib><creatorcontrib>Zhang, Zherui</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>Wireless communications and mobile computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kang, Jian</au><au>Mu, Hui</au><au>Ren, Hui</au><au>Jia, Jicheng</au><au>Qi, Lin</au><au>Zhang, Zherui</au><au>Liu, Mingqian</au><au>Mingqian Liu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Electromagnetic Signal Intelligent Identification Based on Radio Frequency Fingerprints</atitle><jtitle>Wireless communications and mobile computing</jtitle><date>2022-06-07</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>1530-8669</issn><eissn>1530-8677</eissn><abstract>Due to the open nature of WIFI connection, it is exposing its private information to the attackers. Traditional WIFI security methods are no longer able to meet the current security needs, and more and more wireless-side physical layer security solutions provide solutions, among which RF fingerprinting is an endogenous security technology with potential. Constructing an effective and accurate method to identify WIFI devices that steal information is a difficulty that today’s society needs to face. The main problem is not only that the recognition accuracy is difficult to improve but also the problem of data shortage. In this paper, we first construct a large-scale WIFI real-world measurement dataset. Next, we use PSD and bispectrum features, as well as complex ResNet schemes for WIFI device identification experiments, and compare and analyze them from multiple perspectives. The experimental results show that the proposed algorithm can achieve up to 97% recognition accuracy among 100 devices. Moreover, when the SNR is 0 dB, the complex ResNet method can still achieve 78% recognition accuracy among 100 devices. Finally, this paper summarizes the experimental analysis of the measured dataset and discusses the open issues related to this area.</abstract><cop>Oxford</cop><pub>Hindawi</pub><doi>10.1155/2022/6296954</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-3393-0944</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1530-8669 |
| ispartof | Wireless communications and mobile computing, 2022-06, Vol.2022, p.1-12 |
| issn | 1530-8669 1530-8677 |
| language | eng |
| recordid | cdi_proquest_journals_2678218690 |
| source | Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Algorithms Communication Communications networks Datasets Deep learning Engineering Fault diagnosis Fingerprinting Identification methods Identification systems Internet of Things Network security Radiation Security Software Spectrum analysis Wireless networks |
| title | Electromagnetic Signal Intelligent Identification Based on Radio Frequency Fingerprints |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T02%3A28%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Electromagnetic%20Signal%20Intelligent%20Identification%20Based%20on%20Radio%20Frequency%20Fingerprints&rft.jtitle=Wireless%20communications%20and%20mobile%20computing&rft.au=Kang,%20Jian&rft.date=2022-06-07&rft.volume=2022&rft.spage=1&rft.epage=12&rft.pages=1-12&rft.issn=1530-8669&rft.eissn=1530-8677&rft_id=info:doi/10.1155/2022/6296954&rft_dat=%3Cproquest_cross%3E2678218690%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2678218690&rft_id=info:pmid/&rfr_iscdi=true |