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Communication Traffic Characteristics Reveal an IoT Devices Identity
In this paper, a supervised machine learning-based device fingerprinting (DFP) model has been proposed for identifying network-connected IoT devices using only communication traffic characteristics (or implicit identifiers).
Internet of Things: Digital Footprints Carry A Device Identity
In this paper, a device fingerprinting (DFP) model, which is able to distinguish between Internet of Things (IoT) and non-IoT devices, as well as uniquely identify individual devices, has been proposed.
Device identification using optimized digital footprints
In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network.
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising
Despite substantial advances in network architecture performance, the susceptibility of adversarial attacks makes deep learning challenging to implement in safety-critical applications.
Transfer learning for cancer diagnosis in histopathological images
Transfer learning allows us to exploit knowledge gained from one task to assist in solving another but relevant task.
