This conference presentation lays out four new deep neural networks for classifying WiFi physical fingerprints, in attempts to address WiFi susceptibility to security breaches by adversarial actors mimicking Media Access Controller addresses of currently connected devices.
In this conference presentation, the researchers describe four new deep neural networks (NNs) for classifying WiFi physical fingerprints: a real-valued deep NN; a corresponding complex-valued deep NN; a real-valued deep convolutional NN (CNN); and the corresponding complex-valued deep CNN. These efforts attempt to prevent the problem of WiFi susceptibility to security breaches by adversarial actors who mimic Media Access Controller (MAC) addresses of currently connected devices by classifying devices according to their physical fingerprints due to the fact that fingerprints are unique for each device as well as independent of MAC addresses. Results show state-of-the-art performance against a dataset of nine WiFi devices.
Downloads
Related Datasets
Similar Publications
- “They had to change the model to fit the victim, versus the victim having to fit the model”: Innovative solutions in community response to commercial sexual exploitation
- High-Contrast Aptamer-Based Merocyanine Displacement Assays for Sensitive Small Molecule Detection
- The Effects of a Co-Response Program on Patrol Call Volume for Mental Health Crisis-Related Calls: A Time Series Analysis