Communications
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.
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1211 Views
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85 Views
Design of novel RF front-end hardware architectures and their associated measurement algorithms.
Research objectives, includes:
RO1: Novel architecture based upon Adaptive Wavelet Band-pass Sampling (AWBS) of RF Analog-to-Information Conversion (AIC).
RO2: Integration of AWBS for increasing the wideband sensing capabilities of real-time spectrum analyzers by using AICs.
RO3: Propose online calibration methods and algorithms for front-end hardware non-idealities compensation.
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268 Views
This dataset is related to the paper "A distributed Front-end Edge node assessment method by using a learning-to-rank method"
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185 Views
Radio frequency identification (RFID) provides a simple and effective solution to the passive indoor localization. The conventional wisdom about RFID localization is utilizing reference tags. It performs well in tag or single passive target localization. However, in the passive multiple target scenario, reference tag based localization suffers from some limitations, including the array aperture, mutual coupling of reference tags, and coherent superimposition of target signals.
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553 Views
The dataset contains a set of voip flows (with different codecs) captured in fixed/fixed and fixed/mobile LTE-A enviroment.
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1009 Views
This dataset provides the source data for the figures and tables found in the paper "Elliptic Curve Lightweight Cryptography: a Survey."
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593 Views
The dataset was constructed by capturing real-time background traffic of 9 applications. The 9 applications represent different types of network behaviour in the background, for high level of network
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695 Views
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80 Views