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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Datasets for Deep Learning Based Radio Frequency Side-Channel Attack on Quantum Key Distribution

Authors: Baliuka, Adomas; Stöcker, Markus; Auer, Michael; Freiwang, Peter; Weinfurter, Harald; Knips, Lukas;

Datasets for Deep Learning Based Radio Frequency Side-Channel Attack on Quantum Key Distribution

Abstract

The dataset contains measurements of radio-frequency electromagnetic emissions from a home-built sender module for BB84 quantum key distribution. The goal of these measurements was to evaluate information leakage through this side-channel. This dataset supplements our publication and allows to reproduce our results together with the source code hosted at GitHub (and also on Zenodo via integration with GitHub).The measurements are performed using a magnetic near-field probe, an amplifier and an oscilloscope. The dataset contains raw measured data in the file format output by the oscilloscope. Use our source code to make use of it. Detailed descriptions of measurement procedure can be found in our paper and in the metadata JSON files found within the dataset. Commented list of datasets This file lists the datasets that were analyzed and reported on in the paper. The datasets in the list refer to directories here. Note that most of the datasets contain additional files with metadata, which detail where and how the measurements were performed. The mentioned Jupyter notebooks refer to the source code repository https://github.com/XQP-Munich/EmissionSecurityQKD (not included in this dataset). Most of those notebooks output JSON files storing results. The processed JSON files are also included in the source code repository. In naming of datasets, Antenna refers to the log-periodic dipole antenna. All datasets that do not contain `Antenna` in their name are recorded with the magnetic near-field probe. Rev1 refers to the initial electronics design, while `rev2` refers to the revised electronics design which contains countermeasures aiming to reduce emissions. Shielding refers to measurements where the device is enclosed in a metallic shielding and the measurement takes place outside the shielding. Rotation refers to orientation of the magnetic near-field probe at the same spacial location Datasets collected with near-field probe for Rev1 electronics Rev1Distance: contains measurements at different distances from the Rev1 electronics performed above the FPGA. The deep learning attack is analyzed in `TEMPEST_ATTACK.ipynb`. The amplitude is analyzed in `get_raw_data_RMS_amplitude.ipynb`. Rev12D: different locations on a 2d grid at a constant distance from the electronics. The deep learning attack is analyzed in `TEMPEST_ATTACK.ipynb`. Rev130meas2.5cm: 30 measurements above the FPGA at a hight of 2.5cm. Used to evaluate how much amount of training data affects neural network performance. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`. In particular, `TEMPEST_ATTACK_VARY_TRAINING_DATA.ipynb` is used on this dataset. Rev1Rotation10deg contains a measurement for varying orientation of the probe at the same location. This is not mentioned in the paper and is only included for completeness. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`. Rev1TEMPESTShieldingFPGA Measurements with and without shielding at 4cm above the FPGA. - Rev1TEMPESTShieldingUSBHole Measurements with shielding in front of a hole of size about 2cm x 2cm. The deep learning attack is analyzed in `TEMPEST_ATTACK*.ipynb`. Datasets collected with near-field probe for Rev2 electronics Rev2Distance contains measurements at different distances from the Rev2 electronics performed above the FPGA. Rev22D and Rev22Dstart_7_0 contain measurements on a 2d grid performed on the revised electronics. The dataset is split in two directories because the measurement procedure crashed in the middle. This split structure was kept in order to maintain consistency with the automatic metadata. Rev230meas2.5cm 30 measurements above the FPGA at a hight of 2.5cm. Used to evaluate how much amount of training data affects neural network performance. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`. In particular, `TEMPEST_ATTACK_VARY_TRAINING_DATA.ipynb` is used on this dataset. Other datasets BackgroundTuesday background measurement (QKD device is not powered at all) performed with near-field probe on 2022 June 21st. BackgroundSaturday background measurement (QKD device is not powered at all) performed with near-field probe on 2022 June 11th. AntennaSpectra Dataset of spectra directly recorded by the oscilloscope. Used to demonstrate ability of telling apart the situation of sending QKD key (standard operation) and having the device turned on but not sending any key at a distance. Analyzed in notebook `Comparing_KeyNokey_Measurements.ipynb`. Rev2ShieldingAntenna Raw amplitude measurements with log-periodic dipole antenna on Rev2 electronics including shielding enclosure, collected at various distances. None of our attacks against this scenario were successful. The dataset represents a challenge to test more advanced attacks using improved data processing.

Keywords

QKD, Quantum Key Distribution, TEMPEST, Hardware Security, Emission Security

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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