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Improving RF-DNA Fingerprinting Performance in an Indoor Multipath Environment Using Semi-Supervised Learning by Fadul, Mohamed k.; Reising, Donald R.; Weerasena, Lakmali P.; Loveless, T. Daniel; Sartipi, Mina is a scholarly article available to read on EtoBox.

What is Improving RF-DNA Fingerprinting Performance in an Indoor Multipath Environment Using Semi-Supervised Learning about?

The number of Internet of Things (IoT) deployments is expected to reach 75.4 billion by 2025. Roughly 70% of all IoT devices employ weak or no encryption; thus, putting them and their connected infrastructure at risk of attack by devices that are wrongly authenticated or not authenticated at all. A physical layer security approach -- known as Specific Emitter Identification (SEI) -- has been proposed and is being pursued as a viable IoT security mechanism. SEI is advantageous because it is a passive technique that exploits inherent and distinct features that are unintentionally added to the signal by the IoT Radio Frequency (RF) front-end. SEI's passive exploitation of unintentional signal features removes any need to modify the IoT device, which makes it ideal for existing and future IoT deployments. Despite the amount of SEI research conducted, some challenges must be addressed to make SEI a viable IoT security approach. One challenge is the extraction of SEI features from signals collected under multipath fading conditions. Multipath corrupts the inherent SEI features that are used to discriminate one IoT device from another; thus, degrading authentication performance and increa

Author

Fadul, Mohamed k.; Reising, Donald R.; Weerasena, Lakmali P.; Loveless, T. Daniel; Sartipi, Mina

Published

2023

Language

EN