An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting

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dc.contributor.authorBihl, Trevor
dc.contributor.authorTemple, Michael
dc.contributor.authorBauer, Kenneth
dc.date.accessioned2016-12-29T00:57:05Z
dc.date.available2016-12-29T00:57:05Z
dc.date.issued2017-01-04
dc.description.abstractZ-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.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2017.288
dc.identifier.isbn978-0-9981331-0-2
dc.identifier.urihttp://hdl.handle.net/10125/41444
dc.language.isoeng
dc.relation.ispartofProceedings of the 50th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectlearning vector quantization
dc.subjectRF Fingerprinting
dc.subjectoptimization
dc.subjectResponse Surface Methods
dc.subjectSecurity
dc.titleAn Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting
dc.typeConference Paper
dc.type.dcmiText

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