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Dimensional Reduction Analysis for Constellation-Based DNA Fingerprinting to Improve Industrial IoT Wireless Security

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Title:Dimensional Reduction Analysis for Constellation-Based DNA Fingerprinting to Improve Industrial IoT Wireless Security
Authors:Rondeau, Christopher M.
Temple, Michael
Betances, J. Addison
Keywords:Cyber Threat Intelligence and Analytics
Software Technology
DNA Fingerprinting, Industrial IoT, Random Forest, Wilcoxon Rank Sum, ZigBee
Date Issued:08 Jan 2019
Abstract:The Industrial Internet of Things (IIoT) market is skyrocketing towards 100 billion deployed devices and cybersecurity remains a top priority. This includes security of ZigBee communication devices that are widely used in industrial control system applications. IIoT device security is addressed using Constellation-Based Distinct Native Attribute (CB-DNA) Fingerprinting to augment conventional bit-level security mechanisms. This work expands upon recent CB-DNA “discovery” activity by identifying reduced dimensional fingerprints that increase the computational efficiency and effectiveness of device discrimination methods. The methods considered include Multiple Discriminant Analysis (MDA) and Random Forest (RndF) classification. RndF deficiencies in classification and post-classification feature selection are highlighted and addressed using a pre-classification feature selection method based on a Wilcoxon Rank Sum (WRS) test. Feature down-selection based on WRS testing proves to very reliable, with reduced feature subsets yielding cross-device discrimination performance consistent with full-dimensional feature sets, while being more computationally efficient.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/60149
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.856
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Cyber Threat Intelligence and Analytics


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