An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting
Z-Wave is low-power, low-cost Wireless Personal Area Network (WPAN) technology supporting Critical Infrastructure (CI) systems that are interconnected by government-to-internet pathways. Given that Z-wave is a relatively unsecure technology, Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting is considered here to augment security by exploiting statistical features from selected signal responses. Related RF-DNA efforts include use of Multiple Discriminant Analysis (MDA) and Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifiers, with GRLVQI outperforming MDA using empirically determined parameters. GRLVQI is optimized here for Z-Wave using a full factorial experiment with spreadsheet search and response surface methods. Two optimization measures are developed for assessing Z-Wave discrimination: 1) Relative Accuracy Percentage (RAP) for device classification, and 2) Mean Area Under the Curve (AUCM) for device identity (ID) verification. Primary benefits of the approach include: 1) generalizability to other wireless device technologies, and 2) improvement in GRLVQI device classification and device ID verification performance.
Proceedings of the 50th Hawaii International Conference on System Sciences
Bihl, T. J., Temple, M. A., & Bauer, K. W. (2017). An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting. In Proceedings of the 50th Hawaii International Conference on System Sciences (pp. 2379–2387). http://hdl.handle.net/10125/41444