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15 results for “Encrypted”
HIKARI-2021: Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
<p>Available datasets from the paper Generating Encrypted Network Traffic for Intrusion Detection Datasets.</p> <p>To produce the dataset follow the technical detail in <a href="https://github.com/andreysfc/generating-encrypted-network">github</a></p>
Data set for the manuscript 'Polarization-controlled chromo-encryption'
<p>In this dataset, there are 1 pdf and 2 zip files.</p> <p>The manuscript (<strong><em>Zenodo OpenData for chromo encryption<em>.</em>pdf</em></strong>) contains the figures of simulated and measured spectra.</p> <p>The corresponding raw data can refer to the 2 zip files (<strong><em>Simulated.zip </em></strong>and<strong><em> </em></strong><strong><em>Measured<em>.</em>pdf</em></strong>).</p>
Identity Based Proxy Re-encryption Source Code for Security and Privacy in Connected Vehicle
<p>This is a source code for identity based proxy re-encryption using special string attribute for connected vehicle and Privacy, designed in python using Charm Cryptographic library using Pairing Group ss512 and 1024 bits.</p>
Supporting Material to "Statistical Fault Attacks on Nonce-Based Authenticated Encryption Schemes"
<p>supporting_material_code/setX/ct_fault.txt: each line corresponds to one ciphertext received from the device under test while encrypting a plaintext. The bytes are separated by a comma. For every encryption a fault has been injected:</p> <p>set1: Laser fault injections targeting an AES co-processor on a smartcard microcontroller.</p> <p>set2: Clock tampering targeting an AES co-processor implemented on a general-purpose microcontroller.</p> <p>set3 & 4: Clock tampering targeting an AES software implementation (AVR crypto lib ASM) implemented on a general-purpose microcontroller (ATXmega256A3).</p> <p>supporting_material_code/main.cpp: program for key recovery which takes as input the faulty ciphertexts (ct_fault.txt)</p> <p> </p>
Galaxy job runtime measurements with Encrypted and plain storage volumes on Cloud deployments.
<p>Storage volume performance measurement on Cloud environment using Galaxy and Mapping tools: Bowtie2, STAR and Salmon. Galaxy job runtime for encrypted and not ecrypted storage volumes are reported.</p> <p>Scripts and Documentation on GitHub.</p>
Dataset Using TLS Fingerprints for OS Identification in Encrypted Traffic
<p>The dataset consists of data from three different sources; flow records collected from the university backbone network, log entries from the two university DHCP (Dynamic Host Configuration Protocol) servers and a single RADIUS (Remote Authentication Dial In User Service) accounting server. The data was collected from 2019-07-12 00:00 to 2019-07-16 23:59 with a few hours overhead on both sides of the interval for the log entries to cover long connection sessions overlapping to and from the time frame.</p> <p>We measured the flow data from the university uplink to the Internet. In the dataset, we kept only flows with source IP addresses from university wireless networks (Eduroam). The flow data was then enriched with information from DHCP and RADIUS servers to contain ID of the RADIUS session and operating system od the transmitting device as derived from DHCP logs.</p> <p>The dataset is in the form of CSV file with the following information fields important for OS identification:</p> <ul> <li>Basic flow features <ul> <li>Date flow start - timestamp of flow start</li> <li>Date flow end - timestamp of flow end</li> <li>Src IPv4 - source IPv4 address</li> <li>sPort - source L4 port</li> <li>Dst IPv4 - destination IPv4 address</li> <li>dPort - destination L4 port</li> </ul> </li> <li>Extended TCP/IP parameters <ul> <li>SYN size - the size of the initial SYN packet of a TCP connection (in bytes)</li> <li>TCP win - value of TCP Window size parameter</li> <li>TCP SYN TTL - observed TTL value</li> </ul> </li> <li>HTTP parameters <ul> <li>HTTP Host - hostname from the HTTP request</li> <li>HTTP UA OS - OS identification based on user-agent</li> <li>HTTP UA OS MAJ - OS identification based on user-agent</li> <li>HTTP UA OS MIN - OS identification based on user-agent</li> <li>HTTP UA OS BLD - OS identification based on user-agent</li> </ul> </li> <li>TLS parameters <ul> <li>TLS SNI - Server Name Indication field</li> <li>TLS SNI length - length of SNI in bytes</li> <li>TLS Client Version - TLS client hello Version field</li> <li>Client Cipher Suites - list of supported cipher suites</li> <li>TLS Extension Types - list of extension IDs</li> <li>TLS Extension Lengths - list of extension lengths</li> <li>TLS Elliptic Curves - list of supported curves (or supported groups in TLS1.3)</li> <li>TLS EC Point Formats - list of EC formats</li> </ul> </li> <li>Log based extensions <ul> <li>Session ID - ID of the session to match flows from one device</li> <li>Ground Truth OS - OS name derived from log data</li> </ul> </li> </ul> <p>The observed network traffic contains privacy-sensitive information. Hereby, we declare that the monitored data used for our research were processed in accordance with the EU General Data Protection Regulation 2016/679. The published dataset was anonymized with cryptographic means using Crypto-PAn algorithm to preserve both the scientific value and user privacy.</p> <p>When using this dataset, please cite the original work as follows:</p> <pre><code>@inproceedings{lastovicka2020using, title={Using TLS Fingerprints for OS Identification in Encrypted Traffic}, author={La{\v{s}}tovi{\v{c}}ka, Martin and {\v{S}}pa{\v{c}}ek, Stanislav and Velan, Petr and {\v{C}}eleda, Pavel}, booktitle = {2020 IEEE/IFIP Network Operations and Management Symposium (NOMS 2020)}, doi = {http://dx.doi.org/10.1109/NOMS47738.2020.9110319}, keywords = {OS fingerprinting;passive monitoring;IPFIX;TLS}, isbn = {978-1-7281-4973-8}, pages = {1-6}, publisher = {IEEE Xplore Digital Library}, year = {2020} }</code></pre> <p> </p>
Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings
<p>This is the pseudonymized data of the paper "Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings" by Hiller v. Gärtringen et al. 2024.</p> <p>Each entry represents a unique wireless M-Bus device that was captured during our field study.</p> <p>Manufacturers and serial numbers are mapped to new identifiers.<br>Payload was removed.</p> <p>The meaning of the columns in the data set are:</p> <table> <tbody> <tr> <td><strong>name</strong></td> <td><strong>type and manifestations</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>id</td> <td>integer</td> <td> <p>Unique for each wireless transmitting device.<br>Counting up from 1 to n of devices.</p> </td> </tr> <tr> <td>manufacturer</td> <td> <p>enumeration</p> <ul> <li>MAN1 - MAN16</li> </ul> </td> <td>Pseudonymized manufacturer identifier.</td> </tr> <tr> <td>device type</td> <td> <p>enumeration</p> <ul> <li>heat cost allocator</li> <li>heat meter</li> <li>temperature or humidity sensor</li> <li>warm water meter</li> <li>water meter</li> <li>radio control device</li> <li>smoke detector</li> <li>unknown type</li> </ul> </td> <td>Device types are described in EN 13757-7 Table 13</td> </tr> <tr> <td>number of telegrams</td> <td>integer</td> <td>Number of telegrams received from the device.</td> </tr> <tr> <td>has DLL Encryption</td> <td>boolean</td> <td>Indicating, if the device uses DLL encryption.</td> </tr> <tr> <td>AES mode</td> <td> <p>enumeration</p> <ul> <li>not encrypted (mode 0)</li> <li>AES-CBC static key (mode 5)</li> <li>AES-CBC dynamic key (mode 7)</li> <li>AES-CCM (mode 10)</li> </ul> </td> <td>Indicates the AES encryption mode.</td> </tr> <tr> <td>detected in 2022</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>detected in 2023</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>interpretable</td> <td>boolean</td> <td> <p>Indicates whether we identified the message as interpretable.<br>For a detailed description, see the paper.</p> </td> </tr> </tbody> </table> <p> </p>
Padding Ain't Enough: Assessing the Privacy Guarantees of Encrypted DNS – Subpage-Agnostic Domain Classification Firefox
<p>This dataset contains one part for the "Subpage-Agnostic Domain Classification" section of our FOCI 2020 paper "Padding Ain’t Enough: Assessing the Privacy Guarantees of Encrypted DNS".</p> <p><a href="https://www.usenix.org/conference/foci20/presentation/bushart">https://www.usenix.org/conference/foci20/presentation/bushart</a></p> <p>You can find the source code for this project on GitHub: <a href="https://github.com/jonasbb/padding-aint-enough">https://github.com/jonasbb/padding-aint-enough</a></p> <p>When using this software or our dataset, please cite our FOCI 20 paper.</p> <pre>@inproceedings {PaddingAintEnough, author = {Jonas Bushart and Christian Rossow}, booktitle = {10th {USENIX} Workshop on Free and Open Communications on the Internet ({FOCI} 20)}, month = aug, publisher = {{USENIX} Association}, title = {Padding Ain{\textquoteright}t Enough: Assessing the Privacy Guarantees of Encrypted {DNS}}, year = {2020}, }</pre>
Padding Ain't Enough: Assessing the Privacy Guarantees of Encrypted DNS – Web Scans
<p>This dataset contains the main data set of our FOCI 2020 paper "Padding Ain’t Enough: Assessing the Privacy Guarantees of Encrypted DNS".</p> <p><a href="https://www.usenix.org/conference/foci20/presentation/bushart">https://www.usenix.org/conference/foci20/presentation/bushart</a></p> <p>You can find the source code for this project on GitHub: <a href="https://github.com/jonasbb/padding-aint-enough">https://github.com/jonasbb/padding-aint-enough</a></p> <p>When using this software or our dataset, please cite our FOCI 20 paper.</p> <pre>@inproceedings {PaddingAintEnough, author = {Jonas Bushart and Christian Rossow}, booktitle = {10th {USENIX} Workshop on Free and Open Communications on the Internet ({FOCI} 20)}, month = aug, publisher = {{USENIX} Association}, title = {Padding Ain{\textquoteright}t Enough: Assessing the Privacy Guarantees of Encrypted {DNS}}, year = {2020}, }</pre>
Padding Ain't Enough: Assessing the Privacy Guarantees of Encrypted DNS – Subpage-Agnostic Domain Classification Tor Browser
<p>This dataset contains the second part of the "Subpage-Agnostic Domain Classification" section of our FOCI 2020 paper "Padding Ain’t Enough: Assessing the Privacy Guarantees of Encrypted DNS".</p> <p><a href="https://www.usenix.org/conference/foci20/presentation/bushart">https://www.usenix.org/conference/foci20/presentation/bushart</a></p> <p>You can find the source code for this project on GitHub: <a href="https://github.com/jonasbb/padding-aint-enough">https://github.com/jonasbb/padding-aint-enough</a></p> <p>When using this software or our dataset, please cite our FOCI 20 paper.</p> <pre>@inproceedings {PaddingAintEnough, author = {Jonas Bushart and Christian Rossow}, booktitle = {10th {USENIX} Workshop on Free and Open Communications on the Internet ({FOCI} 20)}, month = aug, publisher = {{USENIX} Association}, title = {Padding Ain{\textquoteright}t Enough: Assessing the Privacy Guarantees of Encrypted {DNS}}, year = {2020}, }</pre>
Performance evaluation artefacts for in-memory encryption using the advanced encryption standard
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Benchmark circuits used in the work entitled "Sequential Logic Encryption with Dynamic Keys"
<p>Benchmark circuits used in the work entitled "Sequential Logic Encryption with Dynamic Keys".<br> The original benchmarks are from LGSynth’91 (downloaded from https://ddd.fit.cvut.cz/prj/Benchmarks/).</p> <p>Contents:<br> oriCircuit: original benchmark circuits.<br> k5r5/circuit: encrypted circuits with k=5 and r=5.<br> k5r5/key: secret keys of the encrypted circuits with k=5 and r=5.<br> k5r10/circuit: encrypted circuits with k=5 and r=10.<br> k5r10/key: secret keys of the encrypted circuits with k=5 and r=10.<br> k10r5/circuit: encrypted circuits with k=10 and r=5.<br> k10r5/key: secret keys of the encrypted circuits with k=10 and r=5.<br> k10r10/circuit: encrypted circuits with k=10 and r=10.<br> k10r10/key: secret keys of the encrypted circuits with k=10 and r=10.<br> k15r5/circuit: encrypted circuits with k=15 and r=5.<br> k15r5/key: secret keys of the encrypted circuits with k=15 and r=5.<br> k15r10/circuit: encrypted circuits with k=15 and r=10.<br> k15r10/key: secret keys of the encrypted circuits with k=15 and r=10.</p>
An-electronic-voting-scheme-based-on-homomorphic-encryption-and-decentralization
<p>Calculate the time for a power multiplication operation and compare the time cost of the Counting Center. This cost_e.py files is calculating the time for a power multiplication operation and the figure3.py files is comparing the time cost of the Counting Center. This data_Nv files is the number of the voter.</p>
Encrypted traffic with ESP and TLS
<p>Encrypted traffic with ESP and TLS packets</p>
Optimation of image encryption using fractal Tromino and polynomial Chebyshev based on chaotic matrix
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.