An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting
Document Type
Conference Proceeding
Publication Date
1-2017
Abstract
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.
DOI
10.24251/HICSS.2017.288
Source Publication
Proceedings of the 50th Hawaii International Conference on System Sciences
Recommended Citation
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
Comments
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