Files and links (1)
PDFOpen Access (License Unspecified), Open Access
Thesis
Semi-Supervised Machine Learning Based RF Node Authentication Using Properties of the Wireless Channel
Master of Science (M.S.), Drexel University
Jun 2020
DOI:
https://doi.org/10.17918/00000068
Abstract
Wireless authentication of devices on local networks have relied on the use of unique identifiers, such as MAC and IP addresses, in order to validate devices. These methods of authentication in particular are weak as the addresses are easily attainable and can be changed. Unique identifiers must be resistant to tampering and either impossible or impractical to spoof to be effective authentication methods. Recently there has been a surge in research involving the usage of wireless channel properties as a fingerprint for devices. Node identification based on the wireless channel is of great interest to the research community, due to the unique and complex channel characteristics which vary between devices.
Metrics
43 File views/ downloads
41 Record Views
Details
- Title
- Semi-Supervised Machine Learning Based RF Node Authentication Using Properties of the Wireless Channel
- Creators
- Martin Kraus
- Contributors
- Kapil R. Dandekar (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- xi, 70 pages
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- College of Engineering (1970-2026); Electrical (and Computer) Engineering [Historical]; Drexel University
- Other Identifier
- 991014695138004721