Side-Channel Attacks on Ethernet Cables
2026 (English) Independent thesis Basic level (professional degree), 10 credits / 15 HE credits
Student thesis
Abstract [en]
As cyber threats evolve, unconventional attack vectors such as side-channel techniques arebecoming increasingly relevant in modern security research. This bachelor thesis investigateswhether electromagnetic and thermal emissions from modern Ethernet cables can be used todetect the volume of network traffic from a distance. To address this research question,controlled experiments were conducted using instruments like RTL-SDR, Keysight spectrumanalyzer, and FLIR thermal cameras. Measurements of electromagnetic and thermal emissionswere collected before, during, and after network traffic transmission in a controlledenvironment. Multiple tests were conducted with different network traffic load. For theenvironment, we used different techniques to minimise the impact on the Ethernet cables beingtested by minimising airflow and outer external factors. The results demonstrate a measurablecorrelation between increases in electromagnetic emissions and thermal emissions from theEthernet cable. As this bachelor thesis is, to the best of our knowledge, the first study on thisproblem, more rigorous tests are needed to draw an exact conclusion to the problemformulation. To conclude, state of the practice equipment was used to investigate the possibilityand to what extent the volume of Ethernet traffic can be picked up from a distance. But furtherwork is required to determine whether a quantifiable relationship exist. These findings highlighta potential side-channel vulnerability in Ethernet based communication systems.
Place, publisher, year, edition, pages 2026. , p. 47
Keywords [en]
Side-Channel Attacks, Ethernet, Cables, RTL-SDR, Thermal camera, SIGINT, Cat5e, Cat6, Cat7, Spectrum analyzer, iPerf, intelligence gathering, electromagnetic, Keysight, FLIR, FieldFox N9917B, FLIR G635, network traffic, Network Engineering
National Category
Computer Engineering
Identifiers URN: urn:nbn:se:mdh:diva-78619 OAI: oai:DiVA.org:mdh-78619 DiVA, id: diva2:2087540
Subject / course Computer Science
Supervisors
Examiners
2026-09-302026-07-212026-09-30 Bibliographically approved