Comparison of Radio Frequency Distinct Native Attribute and Matched Filtering Techniques for Device Discrimination and Operation Identification
Abstract
The research presented here provides a comparison of the classification, verification, and computational time performance of three techniques to analyze unintentional radio-frequency (RF) emissions (URE) from semiconductor devices for the purposes of device discrimination and operation identification. URE from ten MSP430F5529 16-bit microcontrollers were analyzed using: 1) RF distinct native attributes (RF-DNA) fingerprints paired with multiple discriminant analysis/maximum likelihood (MDA/ML) classification, 2) RF-DNA fingerprints paired with generalized relevance learning vector quantized-improved (GRLVQI) classification, and 3) time domain (TD) signals paired with matchedfiltering. These techniques were considered for potential applications to detect counterfeit/Trojan hardware infiltrating supply chains and to defend against cyber attacks by monitoring the executed operations of embedded systems in critical supervisory control and data acquisition (SCADA) networks.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 24, 2016
- Accession Number
- AD1053877
Entities
People
- Barron D. Stone
Organizations
- Air Force Institute of Technology