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Tuning Hyperparameters for DNA-based Discrimination of Wireless Devices

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Title:Tuning Hyperparameters for DNA-based Discrimination of Wireless Devices
Authors:Bihl, Trevor
Schoenbeck , Joseph
Rondeau, Christopher
Jones, Aaron
Adams, Yuki
Keywords:Cyber Operations, Defence, and Forensics
digital forensics
neural networks
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Date Issued:05 Jan 2021
Abstract:The Internet of Things (IoT) and Industrial IoT (IIoT) is enabled by Wireless Personal Area Network (WPAN) devices. However, these devices increase vulnerability concerns of the IIoT and resultant Critical Infrastructure (CI) risks. Secure IIoT is enabled by both pre-attack security and post-attack forensic analysis. Radio Frequency (RF) Fingerprinting enables both pre- and post-attack security by providing serial-number level identification of devices through fingerprint characterization of their emissions. For classification and verification, research has shown high performance by employing the neural network-based Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier. However, GRLVQI has numerous hyperparameters and tuning requires AI expertise, thus some researchers have abandoned GRLVQI for notionally simpler, but less accurate, methods. Herein, we develop a fool-proof approach for tuning AI algorithms. For demonstration, Z-Wave, an insecure low-power/cost WPAN technology, and the GRLVQI classifier are considered. Results show significant increases in accuracy (5% for classification, 50% verification) over baseline methods.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Cyber Operations, Defence, and Forensics

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