Location
Hilton Waikoloa Village, Hawaii
Event Website
http://www.hicss.hawaii.edu
Start Date
1-4-2017
End Date
1-7-2017
Description
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.
An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting
Hilton Waikoloa Village, Hawaii
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.
https://aisel.aisnet.org/hicss-50/eg/cybersecurity_and_government/3