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适用于物联网的基于射频的实用跨层认证方案

Practical Cross-Layer Radio Frequency-Based Authentication Scheme for Internet of Things.

作者信息

Haenel Arie, Haddad Yoram, Laurent Maryline, Zhang Zonghua

机构信息

Samovar, Telecom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France.

Department of Computer Science, Jerusalem College of Technology, Jerusalem 91160, Israel.

出版信息

Sensors (Basel). 2021 Jun 11;21(12):4034. doi: 10.3390/s21124034.

DOI:10.3390/s21124034
PMID:34208142
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8230913/
Abstract

The Internet of Things world is in need of practical solutions for its security. Existing security mechanisms for IoT are mostly not implemented due to complexity, budget, and energy-saving issues. This is especially true for IoT devices that are battery powered, and they should be cost effective to be deployed extensively in the field. In this work, we propose a new cross-layer approach combining existing authentication protocols and existing Physical Layer Radio Frequency Fingerprinting technologies to provide hybrid authentication mechanisms that are practically proved efficient in the field. Even though several Radio Frequency Fingerprinting methods have been proposed so far, as a support for multi-factor authentication or even on their own, practical solutions are still a challenge. The accuracy results achieved with even the best systems using expensive equipment are still not sufficient on real-life systems. Our approach proposes a hybrid protocol that can save energy and computation time on the IoT devices side, proportionally to the accuracy of the Radio Frequency Fingerprinting used, which has a measurable benefit while keeping an acceptable security level. We implemented a full system operating in real time and achieved an accuracy of 99.8% for the additional cost of energy, leading to a decrease of only ~20% in battery life.

摘要

物联网领域需要切实可行的安全解决方案。由于复杂性、预算和节能问题,现有的物联网安全机制大多未得到实施。对于使用电池供电的物联网设备来说尤其如此,并且这些设备要具有成本效益才能在现场广泛部署。在这项工作中,我们提出了一种新的跨层方法,将现有的认证协议与现有的物理层射频指纹识别技术相结合,以提供经实际验证在该领域有效的混合认证机制。尽管到目前为止已经提出了几种射频指纹识别方法,作为对多因素认证的支持甚至单独使用,但切实可行的解决方案仍然是一个挑战。即使是使用昂贵设备的最佳系统所取得的准确性结果,在实际系统中仍然不够。我们的方法提出了一种混合协议,该协议可以在物联网设备端节省能源和计算时间,与所使用的射频指纹识别的准确性成比例,在保持可接受的安全水平的同时具有可衡量的益处。我们实现了一个实时运行的完整系统,额外的能源成本使准确率达到了99.8%,导致电池寿命仅减少约20%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/04739b587216/sensors-21-04034-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/19b20f9f2ed3/sensors-21-04034-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/12837fd07dad/sensors-21-04034-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/a362bd3f46c1/sensors-21-04034-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/16350b4741db/sensors-21-04034-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/b6971e14d97f/sensors-21-04034-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/ae95e1a44f73/sensors-21-04034-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/7fb5aed7bab3/sensors-21-04034-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/dbcaa9f1db0e/sensors-21-04034-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/00475ddf228c/sensors-21-04034-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/3164012c352b/sensors-21-04034-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/04739b587216/sensors-21-04034-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/19b20f9f2ed3/sensors-21-04034-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/12837fd07dad/sensors-21-04034-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/a362bd3f46c1/sensors-21-04034-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/16350b4741db/sensors-21-04034-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/b6971e14d97f/sensors-21-04034-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/ae95e1a44f73/sensors-21-04034-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/7fb5aed7bab3/sensors-21-04034-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/dbcaa9f1db0e/sensors-21-04034-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/00475ddf228c/sensors-21-04034-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/3164012c352b/sensors-21-04034-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2668/8230913/04739b587216/sensors-21-04034-g011.jpg

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本文引用的文献

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New Detection Paradigms to Improve Wireless Sensor Network Performance under Jamming Attacks.用于在干扰攻击下提高无线传感器网络性能的新型检测范式。
Sensors (Basel). 2019 May 31;19(11):2489. doi: 10.3390/s19112489.
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Indoor Positioning Algorithm Based on the Improved RSSI Distance Model.基于改进 RSSI 距离模型的室内定位算法。
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