JPG

Device Fingerprinting using Deep Convolutional Neural Networks

Device identification using network traffic analysis is being researched for IoT and non-IoT devices against cyber-attacks. The idea is to define a device specific unique fingerprint by analyzing the solely inter-arrival time (IAT) of packets as feature to identify a device. Deep learning is used on IAT signature for device fingerprinting of 58 non-IoT devices. We observed maximum recall and accuracy of 97.9% and 97.7% to identify device. A comparitive research GTID found using defined IAT signature that models of device identification are better than device type identification.

Categories:
Communications
Security
1262 Views

Arc Flash Phenomena

This set of data provides test results for designers and facility operators to apply in determining the arc-flash hazard distance and the incident energy to which employees could be exposed during their work on or near electrical equipment. 

Categories:
Standards Research Data
Arc Flash
5584 Views

Dataset for testing the performance of colorful FPM reconstruction

This dataset is a human blood smear section FPM dataset for testing the performance of colorful FPM reconstruction. The subdirectories 'R', 'G'  and 'B ' contain images corresponding to red, green and blue light with 30ms exposure time. The subdirectory 'W' contains the multiplex images with 10ms exposure time. The subdirectory 'K' contains one dark-frame for using dark-frame denoising method. The subdirectory '20X' contains high-resolution images captured with a 20X objective lens for comparison.

Categories:
Image Fusion
360 Views

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