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主动式 2D-DNA 无线 HART 适配器指纹识别技术，确保工业系统运行完整性。

Active 2D-DNA Fingerprinting of WirelessHART Adapters to Ensure Operational Integrity in Industrial Systems.

机构信息

Department of Electrical and Computer Engineering, US Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA.

出版信息

Sensors (Basel). 2022 Jun 29;22(13):4906. doi: 10.3390/s22134906.

DOI:10.3390/s22134906
PMID:35808405
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9269693/
Abstract

The need for reliable communications in industrial systems becomes more evident as industries strive to increase reliance on automation. This trend has sustained the adoption of WirelessHART communications as a key enabling technology and its operational integrity must be ensured. This paper focuses on demonstrating pre-deployment counterfeit detection using active 2D Distinct Native Attribute (2D-DNA) fingerprinting. Counterfeit detection is demonstrated using experimentally collected signals from eight commercial WirelessHART adapters. Adapter fingerprints are used to train 56 Multiple Discriminant Analysis (MDA) models with each representing five authentic network devices. The three non-modeled devices are introduced as counterfeits and a total of 840 individual authentic (modeled) versus counterfeit (non-modeled) ID verification assessments performed. Counterfeit detection is performed on a fingerprint-by-fingerprint basis with best case per-device Counterfeit Detection Rate (%CDR) estimates including 87.6% < %CDR < 99.9% and yielding an average cross-device %CDR ≈ 92.5%. This full-dimensional feature set performance was echoed by dimensionally reduced feature set performance that included per-device 87.0% < %CDR < 99.7% and average cross-device %CDR ≈ 91.4% using only 18-of-291 features—the demonstrated %CDR > 90% with an approximate 92% reduction in the number of fingerprint features is sufficiently promising for small-scale network applications and warrants further consideration.

摘要

随着各行业努力提高对自动化的依赖，工业系统对可靠通信的需求变得更加明显。这一趋势推动了 WirelessHART 通信作为关键使能技术的采用，其运行完整性必须得到保证。本文重点介绍了使用主动 2D 独特固有属性 (2D-DNA) 指纹识别技术进行部署前的假冒检测。使用从八个商业 WirelessHART 适配器中采集的实验信号演示了假冒检测。适配器指纹用于训练 56 个多元判别分析 (MDA) 模型，每个模型代表五个真实网络设备。将三个未建模设备作为假冒设备引入，并对总共 840 个真实 (建模) 与假冒 (未建模) ID 验证评估进行了检测。基于指纹的基础上进行了假冒检测，每个设备的最佳假冒检测率（%CDR）估计值包括 87.6%< %CDR <99.9%，平均跨设备的 %CDR≈92.5%。在仅使用 291 个特征中的 18 个的降维特征集性能中也反映了这种全维特征集性能，包括每个设备的 87.0%< %CDR <99.7%和平均跨设备的 %CDR≈91.4%，这表明 %CDR >90%，同时指纹特征数量减少了约 92%，这对于小规模网络应用来说非常有前景，值得进一步考虑。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/a40ec10146bc/sensors-22-04906-g018.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/657df283b715/sensors-22-04906-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/825de940e1f5/sensors-22-04906-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/1267ff7bdb2d/sensors-22-04906-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/9af15e3c06e8/sensors-22-04906-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/970bb1be38a2/sensors-22-04906-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/70d5ce4a70ba/sensors-22-04906-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/41079a833068/sensors-22-04906-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/0226f56ba10e/sensors-22-04906-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/6ab1b5d3acc6/sensors-22-04906-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/fbcbe56011bf/sensors-22-04906-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/61065d853e71/sensors-22-04906-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb6f/9269693/a40ec10146bc/sensors-22-04906-g018.jpg

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