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866 results for “attack”
Figs 53–56 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Figs 53–56. Ceranisus pacuvius (Southampton, England, UK): (53) female antenna, (54) female forewing, (55) male antenna, (56) male genitalia. Scale lines = 0.1 mm.
Figs 49–52 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Figs 49–52. Ceranisus lepidotus: (49, 50) female (paratype): (49) antenna, (50) forewing; (51, 52) male (Valencia, Spain): (51) antenna, (52) genitalia. Scale lines = 0.1 mm.
Figs 31–33 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Figs 31–33. Entedonomphale carbonaria (female – Nagyiván, male – Yácz-Szöd, Hungary): (31) female antenna, (32) female forewing, (33) male antenna. Scale lines = 0.1 mm.
Figs 22, 23 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Figs 22, 23. Entedonomphale mira, female (holotype): (22) antenna, (23) forewing. Scale lines = 0.1 mm.
Figs 17, 18 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Figs 17, 18. Entedonomphale esenini sp. n., female (holotype): (17) antenna, (18) forewing. Scale lines = 0.1 mm.
Figs 59, 60 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Figs 59, 60. Thripobius melikai sp. n., female (paratype): (59) antenna, (60) forewing. Scale lines = 0.1 mm.
Figs 46–48 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Figs 46–48. Ceranisus femoratus, female: (46) antenna (holotype), (47) antenna (Hyderabad, India), (48) forewing (holotype). Scale lines = 0.1 mm.
Figs 57, 58 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Figs 57, 58. Ceranisus sp. 1, male (Mt Tachibanayama, Kyushu Island, Japan): (57) antenna, (58) genitalia. Scale lines = 0.1 mm.
Figs 61–63 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Figs 61–63. Thripobius javae: (61, 62) female (Riverside, California, USA): (61) antenna, (62) forewing; (63) male genitalia (paratype of Thripoctenus maculatus). Scale lines = 0.1 mm.
Figs 26, 27 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Figs 26, 27. Entedonomphale margiscutum, female (holotype): (26) antenna, (27) forewing. Scale lines = 0.1 mm.
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>
Figure 1 in Oomyzus sokolowskii (Hymenoptera: Eulophidae) Joins the Small Complex of Parasitoids Known to Attack the Diamondback Moth on Kauai
Figure 1. Oomyzus sokolowskii reared from Plutella xylostella larvae on kale from Kauai. Female habitus lateral (a) and dorsal (b) view.
Datasets of Man-in-the-middle Attacks Targeting Modbus TCP/IP and MMS protocols in the Smart Grid
<p>The sustainable development of smart grids requires the massive deployment of renewable energy, in a highly distributed manner, introducing new challenges for the system operation. Therefore, the integration of information and communication technologies in sites with Distributed Energy Resources (DERs) is needed to monitor and control the DERs operation. In this scheme, a local controller is installed at each DER site to interact with the centralized applications at the grid level and the power equipment at the site level. This local controller uses client–server protocols (e.g., Modbus TCP/IP and IEC 61850 Manufacturing Message Specification (MMS)) to communicate with different power equipment in the Private Area Network (PAN) of the site. Such protocols often lack information confidentiality and integrity mechanisms. As a result, the smart grids become vulnerable to cyber-attacks. </p> <p>This repository contains datasets created to evaluate the detection and classification of man-in-the-middle attacks, operating in eavesdropping mode, targeting MMS and Modbus TCP/IP protocols in the PAN of the smart grid. Five Flow-based features were used to create these datasets, as shown in Table 1, in addition to the ARP poisoning indicator feature:</p> <table> <caption>Table 1</caption> <tbody> <tr> <td>Feature</td> <td>Description</td> </tr> <tr> <td>IRTT</td> <td>Time for establishing one connection</td> </tr> <tr> <td>TTOC</td> <td>Time for receiving all responses in one connection</td> </tr> <tr> <td>MITR </td> <td>Minimum time between requests in one connection</td> </tr> <tr> <td>MATR </td> <td>Maximum time between requests in one connection</td> </tr> <tr> <td>NROC </td> <td>Number of requests in one connection</td> </tr> </tbody> </table> <p>**NOTE** If you use this dataset in your research/publication please cite us using the following:<br> Mohamed Faisal Elrawy, Lenos Hadjidemetriou, Christos Laoudias, Maria K. Michael,<br> Detecting and classifying man-in-the-middle attacks in the private area network of smart grids,<br> Sustainable Energy, Grids and Networks,2023,pp.1-13, https://doi.org/10.1016/j.segan.2023.101167</p>
philip928lin/Flood-Risks-of-Cyber-physical-Attacks-in-a-Smart-Storm-Water-System: Flood Risks of Cyber-physical Attacks in a Smart Storm Water System
<p>This is the code archive for the publication "Flood Risks of Cyber-physical Attacks in a Smart Storm Water System" in Water Resources Research.</p>
Efficacy and Safety Study of DX-2930 to Prevent Acute Angioedema Attacks in Patients With Type I and Type II HAE
ClinicalTrials.gov study NCT02586805. IPD Sharing: YES. Countries: 7. Publications: 4.
CSL312 (Garadacimab) in the Prevention of Hereditary Angioedema Attacks
ClinicalTrials.gov study NCT04656418. IPD Sharing: YES. Countries: 7. Publications: 2.
A Study of Lanadelumab to Prevent Hereditary Angioedema (HAE) Attacks in Children
ClinicalTrials.gov study NCT04070326. IPD Sharing: YES. Countries: 5. Publications: 1.
A Study of Long-Term Safety and Efficacy of Lanadelumab for Prevention of Acute Attacks of Non-histaminergic Angioedema With Normal C1-Inhibitor
ClinicalTrials.gov study NCT04444895. IPD Sharing: YES. Countries: 10. Publications: 1.
Long-term Safety and Efficacy of CSL312 (Garadacimab) in the Prophylactic Treatment of Hereditary Angioedema Attacks
ClinicalTrials.gov study NCT04739059. IPD Sharing: YES. Countries: 14. Publications: 0.
Study to Evaluate the Clinical Efficacy and Safety of Subcutaneously Administered C1 Esterase Inhibitor for the Prevention of Angioedema Attacks in Adolescents and Adults With Hereditary Angioedema
ClinicalTrials.gov study NCT02584959. IPD Sharing: YES. Countries: 7. Publications: 2.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.