Muhammad Irfan , Javier Hernandez Fernandez , Aymen Omri , Savio Sciancalepore , Gabriele Oligeri
{"title":"Device Fingerprinting in Power Line Communications","authors":"Muhammad Irfan , Javier Hernandez Fernandez , Aymen Omri , Savio Sciancalepore , Gabriele Oligeri","doi":"10.1016/j.adhoc.2025.103955","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div><div>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.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103955"},"PeriodicalIF":4.4000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ad Hoc Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1570870525002033","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to:
Mobile and Wireless Ad Hoc Networks
Sensor Networks
Wireless Local and Personal Area Networks
Home Networks
Ad Hoc Networks of Autonomous Intelligent Systems
Novel Architectures for Ad Hoc and Sensor Networks
Self-organizing Network Architectures and Protocols
Transport Layer Protocols
Routing protocols (unicast, multicast, geocast, etc.)
Media Access Control Techniques
Error Control Schemes
Power-Aware, Low-Power and Energy-Efficient Designs
Synchronization and Scheduling Issues
Mobility Management
Mobility-Tolerant Communication Protocols
Location Tracking and Location-based Services
Resource and Information Management
Security and Fault-Tolerance Issues
Hardware and Software Platforms, Systems, and Testbeds
Experimental and Prototype Results
Quality-of-Service Issues
Cross-Layer Interactions
Scalability Issues
Performance Analysis and Simulation of Protocols.