Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 11 of 11 for “"Specific Emitter Identification"”.
-
On the Use of Convolutional Neural Networks for Specific Emitter Identification
Specific Emitter Identification (SEI) is the association of a received signal to an emitter, and is made possible by the unique and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency (RF) fingerprint. SEI systems are of vital importance to the …
-
Eigenspace Approach to Specific Emitter Identification of Orthogonal Frequency Division Multiplexing Signals
Specific emitter identification is a technology used to uniquely identify a class of wireless devices, and in some cases a single device. Minute differences in the implementation of a wireless communication standard from one device manufacturer to another make it possi- ble to extract a wireless …
-
Addressing the challenges facing deep learning based Specific Emitter Identification via preamble based waveforms
… analyze the effects on Deep Learning (DL) based Specific Emitter Identification (SEI) and address three issues facing the field. SEI is targeted as a physical-layer security measure that can identify radios within an Internet of Things (IoT) deployment without the need of digital credentials. In …
-
Analysis of signal resampling effects on attention-driven SEI for IoT systems
… security, making them vulnerable to attacks. Specific Emitter Identification (SEI) offers a passive and reliable security solution by identifying devices through their unique hardware features, enabling serial number level distinction without altering the emitter. SEI can serve as the …
-
Investigations into the role of entropy-selected RF-DNA fingerprint features on ID-verification performance in the presence of rogue emitters
… security measures against sophisticated threats. Specific Emitter Identification (SEI) is a crucial security approach for authenticating wireless emitters. This work integrates RF-DNA fingerprinting, a specialized form of SEI, with Deep Learning (DL) techniques to authenticate the identity of …
-
Entropy aided RF-DNA fingerprint learning from Gabor-based images
… issue due to the use of weak or no encryption. Specific Emitter Identification (SEI) was introduced to overcome this issue by introduce RF-DNA fingerprinting exploring the PHY layer features. Recently, The SEI performance improved by the usage of the signal’s Time Frequency (TF) representation …
-
Real-World Considerations for RFML Applications
… techniques. Notable among these are the tasks of specific emitter identification (SEI), determining source identity of a received RF signal, and automated modulation classification (AMC), determining the modulation scheme of a received RF transmission. Both tasks have a number of algorithms that …
-
Introducing statistical and machine learning-based methods of enhancing the resiliency and security of electrical-based critical infrastructure
… the physical layer by enabling cross-collection Specific Emitter Identification (SEI) at up to 99.51% blind collection accuracy across eight commercial emitters.
-
Sensitivity Analysis of RFML-based SEI Algorithms
… techniques for the classification tasks of Specific Emitter Identification (SEI) and Automatic Modulation Classification (AMC) have seen rapid improvements in recent years. The applications of SEI, a technique used to associate a received signal to an emitter, and AMC, a technique for …
-
Improving IoT security through the use of deep learning at the physical layer
… fingerprint process capable of exploiting Specific Emitter Identification (SEI) features that are extracted from waveforms that transverse a Rayleigh fading channel prior to collection. This is achieved through the integration of channel correction prior to DL-based radio identification. …
-
Exploiting Cyclostationarity for Radio Environmental Awareness in Cognitive Radios
… shown that pre-allocation of spectrum bands to specific wireless communication applications leads to poor utilization of those allocated bands. Therefore, research into new techniques for efficient spectrum utilization is being aggressively pursued by academia, industry, and government. Such …