A5034031160- Wireless Signal Modulation Classification
- Speech and Audio Processing
- Digital Media Forensic Detection
- Full-Duplex Wireless Communications
- Biometric Identification and Security
- Speech Recognition and Synthesis
- Adversarial Robustness in Machine Learning
- Hate Speech and Cyberbullying Detection
- Radar Systems and Signal Processing
United States Naval Research Laboratory
2018-2019
With the increasing presence of cognitive radio networks as a means to address limited spectral resources, improved wireless security has become necessity. In particular, potential node impersonate licensed user demonstrates need for techniques authenticate radio's true identity. this paper, we use deep learning detect physical-layer attributes identification devices, and demonstrate performance our method on set IEEE 802.15.4 devices. Our is based empirical principle that manufacturing...
A number of successful RF fingerprint classifiers have been demonstrated, but relatively few results evaluate the impact changing receiver used for training and inference. In this work, we record a set 25 ZigBee transmitters with 10 independent, unsynchronized receivers first show that similar performance may be achieved by neural network-based verification system on all when inference are performed same receiver. Next, significant degradation different We propose two methods to address...
As the Internet of Things (IoT) continues to expand, there is a growing necessity for improved techniques authenticate identity wireless transmitters. In this paper, we develop physical-layer authentication technique using neural network structure with both convolutional and recurrent components distinguish transmissions originating from particular target device all others. addition, demonstrate strong performance in realistic multipath channel environment, as well show that classifier...
Recently, a number of neural network approaches to physical-layer wireless security have been introduced. In particular, these are able authenticate the identity different transmitters by device-specific imperfections present in their transmitted signals. this paper, we introduce weakness training protocol approaches, namely, that generative adversarial (GAN) can be trained produce signals realistic enough force classifier errors. We show GAN learn signal without modifying bandwidth or data...