Location
Grand Wailea, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2020 12:00 AM
End Date
10-1-2020 12:00 AM
Description
This paper summarizes demonstration activity aimed at applying Distinct Native Attribute (DNA) feature selection methods to improve the computational efficiency of time domain fingerprinting methods used to discriminate Wireless Highway Addressable Remote Transducer (WirelessHART) devices being used in Industrial (IIoT) applications. Efficiency is achieved through Dimensional Reduction Analysis (DRA) performed here using both pre-classification analytic (WRS and ReliefF) and post-classification relevance (RndF and GRLVQI) feature selection methods. Comparative assessments are based on statistical fingerprint features extracted from experimentally collected WirelessHART signals, with Multiple Discrimination Analysis, Maximum Likelihood (MDA/ML) estimation showing that pre-classification methods are collectively superior to post-classification methods. Specific DRA results show that an average cross-class percent correct classification differential of 8% ≤ %CD ≤ 1% can be maintained using DRA selected feature sets containing as few as 24 (10%) of the 243 full-dimensional features. Reducing fingerprint dimensionality reduces computational efficiency and improves the potential for operational implementation.
DNA Feature Selection for Discriminating WirelessHART IIoT Devices
Grand Wailea, Hawaii
This paper summarizes demonstration activity aimed at applying Distinct Native Attribute (DNA) feature selection methods to improve the computational efficiency of time domain fingerprinting methods used to discriminate Wireless Highway Addressable Remote Transducer (WirelessHART) devices being used in Industrial (IIoT) applications. Efficiency is achieved through Dimensional Reduction Analysis (DRA) performed here using both pre-classification analytic (WRS and ReliefF) and post-classification relevance (RndF and GRLVQI) feature selection methods. Comparative assessments are based on statistical fingerprint features extracted from experimentally collected WirelessHART signals, with Multiple Discrimination Analysis, Maximum Likelihood (MDA/ML) estimation showing that pre-classification methods are collectively superior to post-classification methods. Specific DRA results show that an average cross-class percent correct classification differential of 8% ≤ %CD ≤ 1% can be maintained using DRA selected feature sets containing as few as 24 (10%) of the 243 full-dimensional features. Reducing fingerprint dimensionality reduces computational efficiency and improves the potential for operational implementation.
https://aisel.aisnet.org/hicss-53/st/digital_forensics/2