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.

DOI Link: 
10.21227/d9e4-wm90
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[1] Nagender Aneja, Sandhya Aneja, Md Shohidul Islam, Bharat Bhargava, "Device Fingerprinting using Deep Convolutional Neural Networks", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/d9e4-wm90. Accessed: Jun. 08, 2023.
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author = {Nagender Aneja; Sandhya Aneja; Md Shohidul Islam; Bharat Bhargava },
publisher = {IEEE Dataport},
title = {Device Fingerprinting using Deep Convolutional Neural Networks},
year = {2019} }
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Nagender Aneja, Sandhya Aneja, Md Shohidul Islam, Bharat Bhargava. (2019). Device Fingerprinting using Deep Convolutional Neural Networks. IEEE Dataport. http://dx.doi.org/10.21227/d9e4-wm90
Nagender Aneja, Sandhya Aneja, Md Shohidul Islam, Bharat Bhargava, 2019. Device Fingerprinting using Deep Convolutional Neural Networks. Available at: http://dx.doi.org/10.21227/d9e4-wm90.
Nagender Aneja, Sandhya Aneja, Md Shohidul Islam, Bharat Bhargava. (2019). "Device Fingerprinting using Deep Convolutional Neural Networks." Web.
1. Nagender Aneja, Sandhya Aneja, Md Shohidul Islam, Bharat Bhargava. Device Fingerprinting using Deep Convolutional Neural Networks [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/d9e4-wm90
Nagender Aneja, Sandhya Aneja, Md Shohidul Islam, Bharat Bhargava. "Device Fingerprinting using Deep Convolutional Neural Networks." doi: 10.21227/d9e4-wm90