Device Fingerprinting in Power Line Communications
<p dir="ltr">Power Line Communication (PLC) use existing electrical infrastructure for data transmission but are susceptible to security threats such as spoofing and impersonation attacks due to their open nature. This paper proposes a novel Device Fingerprinting (DF) approach for de...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513541519704064 |
|---|---|
| author | Muhammad Irfan (255476) |
| author2 | Javier Hernandez Fernandez (19418752) Aymen Omri (19418755) Savio Sciancalepore (16864152) Gabriele Oligeri (14151426) |
| author2_role | author author author author |
| author_facet | Muhammad Irfan (255476) Javier Hernandez Fernandez (19418752) Aymen Omri (19418755) Savio Sciancalepore (16864152) Gabriele Oligeri (14151426) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Irfan (255476) Javier Hernandez Fernandez (19418752) Aymen Omri (19418755) Savio Sciancalepore (16864152) Gabriele Oligeri (14151426) |
| dc.date.none.fl_str_mv | 2025-07-01T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.adhoc.2025.103955 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Device_Fingerprinting_in_Power_Line_Communications/29665400 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Communications engineering Information and computing sciences Computer vision and multimedia computation Cybersecurity and privacy Machine learning Power line communications Cybersecurity Device fingerprinting Physical-layer security Deep learning Authentication |
| dc.title.none.fl_str_mv | Device Fingerprinting in Power Line Communications |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Power Line Communication (PLC) use existing electrical infrastructure for data transmission but are susceptible to security threats such as spoofing and impersonation attacks due to their open nature. This paper proposes a novel Device Fingerprinting (DF) approach for device authentication in PLC systems. The approach leverages hardware-induced imperfections in signals transmitted over power lines to identify devices based on their physical-layer characteristics. We develop a methodology that converts raw In-Phase Quadrature (IQ) samples from PLC channels into images, enabling the use of Convolutional Neural Networks for device classification. Our approach demonstrates the feasibility of CNN-based DF in PLC environments using only physical-layer information from received signals. Our experimental validation uses 8 Software Defined Radios and 2 power line couplers in real-world PLC measurements. We evaluate multiple Convolutional Neural Network (CNN) architectures and demonstrate that the PLC device fingerprint consists of two components: radio-specific and coupler-specific characteristics. The results show classification accuracy exceeding 0.9 across different configurations, establishing the viability of DF-based authentication in PLC systems without requiring additional security layers.</p><h2>Other Information</h2><p dir="ltr">Published in: Ad Hoc Networks<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.adhoc.2025.103955" target="_blank">https://dx.doi.org/10.1016/j.adhoc.2025.103955</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_17d65a40bb9aa4c93859a0c9d3d2294c |
| identifier_str_mv | 10.1016/j.adhoc.2025.103955 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29665400 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Device Fingerprinting in Power Line CommunicationsMuhammad Irfan (255476)Javier Hernandez Fernandez (19418752)Aymen Omri (19418755)Savio Sciancalepore (16864152)Gabriele Oligeri (14151426)EngineeringCommunications engineeringInformation and computing sciencesComputer vision and multimedia computationCybersecurity and privacyMachine learningPower line communicationsCybersecurityDevice fingerprintingPhysical-layer securityDeep learningAuthentication<p dir="ltr">Power Line Communication (PLC) use existing electrical infrastructure for data transmission but are susceptible to security threats such as spoofing and impersonation attacks due to their open nature. This paper proposes a novel Device Fingerprinting (DF) approach for device authentication in PLC systems. The approach leverages hardware-induced imperfections in signals transmitted over power lines to identify devices based on their physical-layer characteristics. We develop a methodology that converts raw In-Phase Quadrature (IQ) samples from PLC channels into images, enabling the use of Convolutional Neural Networks for device classification. Our approach demonstrates the feasibility of CNN-based DF in PLC environments using only physical-layer information from received signals. Our experimental validation uses 8 Software Defined Radios and 2 power line couplers in real-world PLC measurements. We evaluate multiple Convolutional Neural Network (CNN) architectures and demonstrate that the PLC device fingerprint consists of two components: radio-specific and coupler-specific characteristics. The results show classification accuracy exceeding 0.9 across different configurations, establishing the viability of DF-based authentication in PLC systems without requiring additional security layers.</p><h2>Other Information</h2><p dir="ltr">Published in: Ad Hoc Networks<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.adhoc.2025.103955" target="_blank">https://dx.doi.org/10.1016/j.adhoc.2025.103955</a></p>2025-07-01T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.adhoc.2025.103955https://figshare.com/articles/journal_contribution/Device_Fingerprinting_in_Power_Line_Communications/29665400CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296654002025-07-01T03:00:00Z |
| spellingShingle | Device Fingerprinting in Power Line Communications Muhammad Irfan (255476) Engineering Communications engineering Information and computing sciences Computer vision and multimedia computation Cybersecurity and privacy Machine learning Power line communications Cybersecurity Device fingerprinting Physical-layer security Deep learning Authentication |
| status_str | publishedVersion |
| title | Device Fingerprinting in Power Line Communications |
| title_full | Device Fingerprinting in Power Line Communications |
| title_fullStr | Device Fingerprinting in Power Line Communications |
| title_full_unstemmed | Device Fingerprinting in Power Line Communications |
| title_short | Device Fingerprinting in Power Line Communications |
| title_sort | Device Fingerprinting in Power Line Communications |
| topic | Engineering Communications engineering Information and computing sciences Computer vision and multimedia computation Cybersecurity and privacy Machine learning Power line communications Cybersecurity Device fingerprinting Physical-layer security Deep learning Authentication |