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13 results for “Side-channel”
USENIX'24 Artifact Datasets: With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors
<p>This dataset contains the measurements and analysis results for our USENIX Security '24 paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors'.</p>
Data used in Prefetch Side-Channel Attacks
<p>Machine-/System-/library-/version-specific data is generated as a part of the attack. The results from measurements cannot directly be applied to other systems/libraries/versions or machines. Therefore we have publish the source code to generate the dataset.</p>
Dragon_Pi: IoT Side-Channel Power Data Intrusion Detection Dataset and Unsupervised Convolutional Autoencoder for Intrusion Detection
<h2><strong>Dragon_Pi</strong></h2> <div> <div>For a more in depth description of the Dragon_Pi dataset, please consult the journal article of the same name:</div> <div>Lightbody <em>et al.</em>, Future Internet, 2024, <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a> - specifically Section 3.2: Dataset Overview.</div> <div> </div> </div> <p>Dragon_Pi is an intrusion detection dataset for IoT devices. In the field of IoT security there are few datasets, and those which do exist tend to focus solely on network traffic. The Dragon_Pi dataset seeks to provide not only more data for the field of IoT security, but also, data of a somewhat under-published type: linear time series power consumption data.</p> <p>Dragon_Pi is a fully labelled Intrusion Detection dataset for IoT devices. It is composed of both normal and under-attack power consumption data obtained from two separate testbeds - one using a DragonBoard 410c and the other a Raspberry Pi Model 3 - Hence the moniker <em>Dragon_Pi</em>. </p> <p>These testbeds were set up with predefined normal behavour as described in the attached publications. The normal linear time series power consumption was sampled from the testbed under these normal conditions. Both testbeds were then attacked using some common attacks on IoT - the linear time series power consumption captured under these condtions as well. </p> <p>Specifically, the testbeds were subjected to the Port Scan (using Nmap), SSH Brute Force (using Hydra) and SYNFlood Denial of Service (using Hping3) attacks. These attacks were repeated to gain insight to what their signatures looked like and also how varying the tool settings effected the resultant signature. A fourth type of scenario was also conducted on the testbeds - the "Capture the Flag" scenarios. In these files multiple attack types were used with a more specific target - to exfiltrate a hidden file from the testbeds.</p> <p>Each file has three hierarchical levels of annotation for <strong>each sample</strong> within:</p> <ol> <li>A simple "Normal or Anomaly" label for the specific sample</li> <li>A specifc attack type label e.g. "SSH Bruteforce", for the specific sample</li> <li>A specific tool setting for that attack e.g. "Hydra_T16", for the specific sample</li> </ol> <p>Users can decide for themselves what level of annotation they require for their specific task. </p> <p>Each file in the Dragon_Pi dataset is accompanied by its own legend file. This file explains the contents of the specific .csv file and the specific indexes of the events within.</p> <p>The Dragon_Pi dataset consists of approximately 67 files, as shown in Table 1. Compressed, the datset totals approximately 13GB. Completely decompressed the dataset is approximately 80GB ( 30GB Pi data, 50 GB Dragon data). </p> <div> </div> <div> <table> <tbody> <tr> <td>Label Type</td> <td>Specific Label </td> <td>Number of Files DragonBoard 410c</td> <td>Number of Files Raspberry Pi</td> </tr> <tr> <td>Normal </td> <td>Normal </td> <td>3 </td> <td>2</td> </tr> <tr> <td>Port Scan Attack </td> <td>Nmap_T5</td> <td>2</td> <td>1</td> </tr> <tr> <td> </td> <td>Nmap_T4</td> <td>1</td> <td>1</td> </tr> <tr> <td> </td> <td>Nmap_T3</td> <td>1</td> <td>1</td> </tr> <tr> <td> </td> <td>Nmap_T2</td> <td>1</td> <td>1</td> </tr> <tr> <td>SSH Brute Force</td> <td>Hydra_T32</td> <td>4</td> <td>2</td> </tr> <tr> <td> </td> <td>Hydra_T16</td> <td>16</td> <td>2</td> </tr> <tr> <td> </td> <td>Hydra_T3</td> <td>8</td> <td>2</td> </tr> <tr> <td> </td> <td>Hydra_T1</td> <td>5</td> <td>2</td> </tr> <tr> <td>SYNFlood DOS</td> <td>SYNFlood DOS</td> <td>1</td> <td>1</td> </tr> <tr> <td>Capture the Flag</td> <td>Misc Attacks</td> <td>3</td> <td>5</td> </tr> </tbody> </table> </div> <div>Table 1. Enumeration of the in the Dragon_Pi dataset.</div> <div> </div> <div> </div> <div>For a more in depth description of the Dragon_Pi dataset, please consult the journal article of the same name:</div> <div>Lightbody <em>et al.</em>, Future Internet, 2024, <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a> - specifically Section 3.2: Dataset Overview.</div> <div> </div> <div> </div> <div><strong>Publication of this dataset:</strong></div> <div> </div> <div>This dataset was published in Lightbody <em>et al.</em>, Future Internet, 2024, <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a>. Consult and cite this article for a more in depth dataset description, as well as an in depth review of first AI Intrusion Detection model trained on this dataset. </div> <div> </div> <div>See article Lightbody <em>et al.</em>, Future Internet, 2023, <a href="https://doi.org/10.3390/fi15050187">https://doi.org/10.3390/fi15050187</a> for a detailed investigation on the attack signatures discovered while creating this dataset. This work was an inital investigation of the dataset and can serve as a part 1 to the Dragon_Pi paper.</div> <div> </div> <div> </div> <div><strong>How to cite this dataset in your work: </strong></div> <div> </div> <div>Please cite these two DOIs when publishing using this dataset:</div> <div> <ol> <li>Dragon_Pi release publication: <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a> (most important)</li> <li>Zenodo Dataset DOI: https://doi.org/10.5281/zenodo.10784947</li> </ol> </div> <div> <div> </div> </div> <p> </p>
Dataset for the paper: Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel
<p>This repository contains data related to "Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel," by Riccardo Spolaor, Hao Liu, Federico Turrin, Mauro Conti, Xiuzhen Cheng, to appear in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), 17-20 May 2023.</p> <p>This dataset includes the labels and features extracted from the energy consumption of 82 USB peripherals under different states (i.e., Boot, On) and actions (e.g., Read, Write, Upload, Download). The dataset contains more than 175.000 segments extracted from around 20.000 power traces. We have collected the raw power traces with a National Instruments USB-6210 DAQ at a sampling rate of 10kHz. Each segment is one second long. Please, find more details about the data collection in the paper.<br> We identify a USB peripheral by its type (Device_Type), model (Device_Model), and physical device with such type and model (Device_Id). For each power trace's segment, we assign a unique identifier (Segment_Id), and we indicate the action performed (Action) and the activity/inactivity proportions (Activity_Ratio and Inactive_Ratio). The remaining columns (with the prefix "EC__") are the features extracted from segments using the tsfresh libraries for python V0.19.0 (https://tsfresh.readthedocs.io)</p> <p><strong>Please, support our work by citing our paper:</strong><br> Riccardo Spolaor, Hao Liu, Federico Turrin, Mauro Conti, Xiuzhen Cheng, "Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel," In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), 2023.</p> <p><strong>Contact info:</strong> Riccardo Spolaor (rspolaor@sdu.edu.cn, Shandong University, Qingdao, China) and Federico Turrin (turrin@math.unipd.it, University of Padua, Padua, Italy).</p>
Towards Efficient Training in Deep Learning Side-Channel Attacks
<p>Datasets used to develop my Master's Thesis <em>Towards Efficient Training in Deep Learning Side-Channel Attacks</em> at Politecnico di Milano. </p> <p>The datasets contain power consumption measurements taken from multiple <em>Riscure Piñata </em>(STM32F4) boards (3) considering multiple keys (11) while executing AES-128.</p> <p>unprotected-AES.zip contains the traces related to the execution of a software unprotected implementation of AES-128.</p> <p>masked-AES.zip contains the traces related to the execution of a software masked implementation of AES-128.</p>
Datasets for Deep Learning Based Radio Frequency Side-Channel Attack on Quantum Key Distribution
<p>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 <a href="https://link.aps.org/doi/10.1103/PhysRevApplied.20.054040">publication</a> and allows to reproduce our results together with the source code hosted at <a href="https://github.com/XQP-Munich/EmissionSecurityQKD">GitHub</a> (and also on <a href="https://doi.org/10.5281/zenodo.7965628">Zenodo</a> via integration with GitHub).<br><br>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.</p> <p><strong>Commented list of datasets</strong></p> <p>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.</p> <p>In naming of datasets,</p> <ul> <li><em>Antenna</em> 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.</li> <li><em>Rev1</em> refers to the initial electronics design, while `rev2` refers to the revised electronics design which contains countermeasures aiming to reduce emissions.</li> <li><em>Shielding</em> refers to measurements where the device is enclosed in a metallic shielding and the measurement takes place outside the shielding.</li> <li><em>Rotation</em> refers to orientation of the magnetic near-field probe at the same spacial location</li> </ul> <p><strong>Datasets collected with near-field probe for Rev1 electronics</strong></p> <ul> <li><strong>Rev1Distance</strong>: 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`.</li> <li><strong>Rev12D</strong>: different locations on a 2d grid at a constant distance from the electronics. The deep learning attack is analyzed in `TEMPEST_ATTACK.ipynb`.</li> <li><strong>Rev130meas2.5cm</strong>: 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.</li> <li><strong>Rev1Rotation10deg</strong> 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`.</li> <li><strong>Rev1TEMPESTShieldingFPGA</strong> Measurements with and without shielding at 4cm above the FPGA.</li> <li>- <strong>Rev1TEMPESTShieldingUSBHole</strong> Measurements with shielding in front of a hole of size about 2cm x 2cm. The deep learning attack is analyzed in `TEMPEST_ATTACK*.ipynb`.</li> </ul> <p><strong>Datasets collected with near-field probe for Rev2 electronics</strong></p> <ul> <li><strong>Rev2Distance</strong> contains measurements at different distances from the Rev2 electronics performed above the FPGA.</li> <li><strong>Rev22D</strong> and <strong>Rev22Dstart_7_0</strong> 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.</li> <li><strong>Rev230meas2.5cm</strong> 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.</li> </ul> <p><strong>Other datasets</strong></p> <ul> <li><strong>BackgroundTuesday</strong> background measurement (QKD device is not powered at all) performed with near-field probe on 2022 June 21st.</li> <li><strong>BackgroundSaturday</strong> background measurement (QKD device is not powered at all) performed with near-field probe on 2022 June 11th.</li> <li><strong>AntennaSpectra</strong> 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`.</li> <li><strong>Rev2ShieldingAntenna</strong> 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.</li> </ul> <p> </p>
Dataset for: A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces
<p>This dataset is part of "A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces" available <a href="https://www.arxiv.org/abs/2402.19037" target="_blank" rel="noopener">online</a>.</p> <p>The source code for testing the dataset is available on <a href="https://github.com/hardware-fab/DL-to-locate-COs-for-SCA">GitHub</a>.</p> <p>The dataset is organized as follows:</p> <ul> <li><strong>\training</strong>: contains three subsets, i.e., train, valid, and test. <br> Each subset consists of two .npy files: <ul> <li><em>_set</em>: it contains the side-channel traces that are preprocessed accordingly.</li> <li> <em>_labels</em>: itcontains the target labels for training the CNN, labeling each data as <em>cipher start</em>, <em>cipher rest</em>, or <em>noise</em>.</li> </ul> </li> <li><strong>\inference</strong>: contains two files as a demo of the inference pipeline.<br> One file is the is the side-channel trace containing an undefined number of AES encryptions. The other file is a list of plaintexts matching the AES encryptions to test a CPA attack.</li> </ul> <p><strong>Cite:</strong></p> <blockquote> <pre><code>@INPROCEEDINGS{10546758, author={Chiari, Giuseppe and Galli, Davide and Lattari, Francesco and Matteucci, Matteo and Zoni, Davide}, booktitle={2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)}, title={A Deep- Learning Technique to Locate Cryptographic Operations in Side-Channel Traces}, year={2024}, pages={1-6}, doi={10.23919/DATE58400.2024.10546758}}</code></pre> </blockquote> <p>This repository is protected by copyright and licensed under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.</p> <p>© 2024 hardware-fab</p>
Dataset for: Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces Using Deep-Learning
<p>This dataset is part of "Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces Using Deep-Learning" [1] available <a href="https://arxiv.org/pdf/2408.06296">online</a>.</p> <p>The source code for testing the dataset is available on <a href="https://github.com/hardware-fab/Hound">GitHub</a>.</p> <p>The dataset is organized as follows:</p> <ul> <li><strong>/training</strong>: Contains three subsets: <em>train</em>, <em>valid</em>, and <em>test</em>. Each subset consists of two <em>.npy</em> files: <ul> <li><em><strong>_set</strong></em>: Contains the preprocessed side-channel traces.</li> <li><strong><em>_labels</em></strong>: Contains the target labels for training the CNN, labeling each data as `CP start`, `CP spare`, or `noise`.</li> </ul> </li> <li><strong>/inference</strong>: Contains files for two demos: consecutive AES executions and AES executions interleaved with noisy applications. Each demo consists of two <em>.npy</em> files: <ul> <li><strong>aes_</strong>: Contains the side-channel traces to input into Hound.</li> <li><strong>gt_</strong>: Contains the ground truth for checking the correctness of Hound segmentation.</li> </ul> </li> </ul> <p>This repository is protected by copyright and licensed under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.</p> <p>© 2024 hardware-fab</p> <blockquote> <p>[1] D. Galli, G. Chiari and D. Zoni, "Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces using Deep-Learning," 2024 IEEE 42nd International Conference on Computer Design (ICCD), Milan, Italy, 2024, pp. 114-121, doi: 10.1109/ICCD63220.2024.00027.</p> </blockquote>
Side-channel analysis on masked-AES implementation on ARM M3
<p>A data set of power traces that was acquired from executing a two-shares masked AES SubBytes implementation (written in Thumb Assembly Language) on an ARM Cortex M3 processor core from NXP (LPC1313). This implies that no single point leaks information about the unshared intermediate value, further confirmed via leakage detection. </p> <p>We use a custom measurement board (Picoscope 5243D), which provides good measurements (at 250 MSa/s, and the working frequency is set to 2 MHz). We use our scope in a basic setting to avoid any trace processing (de-noising) and extract discrete traces (5 million), where each point is represented by 8 bits.</p> <p>We have uploaded a compressed version of .trs file.</p> <p>We have used the data for our paper "Leakage Certification Made Simple"</p>
Datatset: Machine-Learning Side-Channel Attacks on the GALACTICS Constant-Time Implementation of BLISS
<p>This dataset accompanies the paper "Machine-Learning Side-Channel Attacks on the GALACTICS Constant-Time Implementation of BLISS". It was used to experimentally prove the presented attack strategies on real hardware. The corresponding source code for all three attacks is also publicly available.</p> <p>A detailed description of how the data was obtained can be found in the paper. Section 4 precisely describes the experimental setup.</p> <p> </p> <p>Prerequisites:</p> <pre><code class="language-bash">sudo apt-get install p7zip</code></pre> <p> </p> <p>Extract the data:</p> <pre><code class="language-bash">7z x galactics_attack_data.7z</code></pre> <p> </p> <p>Running the attacks:</p> <p>The source code to run the three presented attacks can be found on Github. The instructions on how to use the python code can be obtained from the corresponding README.</p> <p> </p> <p>Re-using the dataset:</p> <p>The dataset consists of <em>.pickle</em> and <em>.bin</em> files. The <em>.pickle</em> files can be read using <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_pickle.html">Pythons Pandas library</a>. Python access functions for the <em>.bin</em> files are also provided.</p>
Data of Black-Box Side-Channel Detection and Mitigation for Internet of Things
<p>This repository contains the datasets used in the experiments of the paper "Black-Box Side-Channel Detection and Mitigation for Internet of Things" and the results of the experiments.</p>
TCHES artefact dataset for paper : Efficient Regression-Based Linear Discriminant Analysis for Side-Channel Security Evaluations
<p>This dataset allows reproducing the results of the CHES 2023 paper : "Efficient Regression-Based Linear Discriminant<br> Analysis for Side-Channel Security Evaluations".</p> <p>It contains the side-channel measurements necessary to do so.</p> <p>Scripts and readme is available at the CHES artefact site : <link not yet alive></p> <p> </p>
A Systematic Evaluation of Automated Tools for Side-Channel Vulnerabilities Detection in Cryptographic Libraries
<p>Artifact for CCS'23 submission</p>
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