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57 results for “ssh”
HYCOM mode 1 steric SSH M2 amplitude and phase
<p>This data set contains data from a forward global HYCOM simulation (22.1) with realistic tide and atmospheric forcing as discussed in <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a> (On the interplay between horizontal resolution and wave drag and their effect on tidal baroclinic mode waves in realistic global ocean simulations, 2020, MC Buijsman, GR Stephenson, JK Ansong, BK Arbic, JAM Green, ... Ocean Modelling 152, 101656). <strong>Please cite this article when using these data. </strong></p> <p>This is a 4-km simulation with 41 layers. All data is on the native tripole grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).</p> <p>The data set contains</p> <ol> <li>the M2 mode 1 complex amplitude (RE+i*IM) of steric SSH computed for a two-week time series; the phase is relative to GMT 01-Sep-2016 01:00:00; the mode-1 amplitude is computed from the mode-1 surface pressure value (see <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a>). </li> <li>Mode-1 eigenspeed</li> <li>Positive seafloor depth, and latitude and longitude coordinates</li> </ol> <p>To convert eigenspeed to wavelength, phase speed and group speed, see <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a></p> <p><a href="https://sites.google.com/site/maartenbuijsman/">https://sites.google.com/site/maartenbuijsman/</a></p>
PANDAcap SSH Honeypot Dataset
<p>This is a dataset of <strong>63 <a href="https://github.com/panda-re/panda">PANDA</a> traces</strong>, collected using the <a href="https://github.com/vusec/pandacap">PANDAcap</a> framework. The dataset aims to offer a starting point for the analysis of <em>ssh brute force attacks</em>. The traces were collected through the course of approximately 3 days from 21 to 23 February 2020. A VM was configured using PANDAcap so that it accepts all passwords for user <code>root</code>. When an ssh session starts for the user, PANDA is signaled by the <a href="https://github.com/panda-re/panda/tree/master/panda/plugins/recctrl">recctrl plugin</a> to start recording for 30'.</p> <p>You can read more details about the experimental setup and an overview of the dataset <strong>EuroSec 2020</strong> publication:</p> <ul> <li> <p>Manolis Stamatogiannakis, Herbert Bos, and Paul Groth. PANDAcap: A Framework for Streamlining Collection of Full-System Traces. In <em>Proceedings of the 13th European Workshop on Systems Security</em>, <a href="https://www.concordia-h2020.eu/eurosec-2020/">EuroSec '20</a>, Heraklion, Greece, April 2020. doi: <a href="https://doi.org/10.1145/3380786.3391396">10.1145/3380786.3391396</a>, preprint: <a href="https://www.vusec.net/publications/#stamatogiannakis-bos-groth-pandacapaframeworkforstreamliningcollectionoffullsystemtraces-2020">vusec.net</a></p> </li> </ul> <p>The dataset is split in 3 zip files/directories:</p> <ul> <li><strong>rr</strong>: Contains the 63 PANDA traces of the dataset. The traces are in the upcoming RRArchive format. Note that PANDA support for the format is still wip at the time of writing (April 2020). If you need to downgrade to the traditional PANDA trace format, you can use the snippet in <a href="https://github.com/vusec/pandacap/blob/master/docs/xxx">foo</a>.</li> <li><strong>qcow</strong>: Contains the QCOW base image (<code>ubuntu16-planb.qcow2</code>) used to create the dataset, as well as the disk deltas for the 63 traces. These can be mounted to inspect the contents of the filesystem before and after each session. and disk deltas for the 63 traces. Quick instructions on how to mount and inspect a QCOW image can be found below.</li> <li><strong>pcap</strong>: Contains the pcap network traces for the sessions in the PANDA traces. These have been extracted using the PANDA <a href="https://github.com/panda-re/panda/tree/master/panda/plugins/network">network plugin</a>. We decided to also include them in the dataset as standalone files for convenience.</li> </ul> <p>Additionally, we provide the PANDA linux kernel profile <code>ubuntu16-planb-kernelinfo.conf</code>, which can be used to analyze the traces using the PANDA <a href="https://github.com/panda-re/panda/tree/master/panda/plugins/osi_linux">osi_linux plugin</a>.</p> <p>Additional information:</p> <ul> <li>To convert RRArchive traces to the traditional PANDA format, run the following snippet inside the <code>rr</code> directory: <pre><code class="language-bash">for f in *.tar.gz; do tar -zxvf "$f" --exclude=PANDArr --xform='s%/%-%' --xform='s%-metadata%%' rm -f "$f" done</code></pre> </li> <li>If you wish to reuse the VM image in your project, it is available as a standalone download through <a href="https://academictorrents.com/details/39df3904460e909e175434cbd87764b8c487891d">academictorrents.com</a>, along with more detailed information on its contents.</li> <li>If you wish to download individual samples rather than the whole dataset, you can use the dataset torrent file available through <a href="https://academictorrents.com/details/4a3eadf47425cb60111ec224de272997294eec93">academictorrents.com</a>. Unlike this Zenodo deposit, the files in the torrent have not been zipped.</li> <li>A better formatted (and possibly more up-to-date) version of this information can be found <a href="https://github.com/vusec/pandacap/blob/master/docs/eurosec20-dataset.md">here</a>.</li> </ul>
Ocean surface currents, SSH and SST from LLC4320, before and after Lagrangian filtering
<p>This dataset comprises daily snapshots of horizontal velocity, sea surface height and sea surface temperature from LLC4320, a high resolution setup of the MITgcm, in the Agulhas region. We provide the unfiltered data, and the data after Lagrangian filtering as described in Jones, CS, Xiao, Q, Abernathey, RP and Smith, KS <em>Separating balanced and unbalanced flow at the surface of the Agulhas region using Lagrangian filtering (preprint: </em><a href="https://doi.org/10.31223/X5D352">https://doi.org/10.31223/X5D352</a> ). Lagrangian filtering is not applied to the sea surface temperature.</p> <p>This dataset is not the dataset that was used to make the figures in Jones et al. (see <a href="https://doi.org/10.5281/zenodo.6574163">https://doi.org/10.5281/zenodo.6574163</a>), but a separate dataset that is meant to be used in future study. We have decided to make this dataset publicly available because it may be useful for machine learning, or for studies that investigate the dynamical equations that govern the sea surface height and horizontal velocity field.</p> <p>unfilt_u_v_ssh_sst.nc contains unfiltered horizontal velocity, sea surface height and sea surface temperature</p> <p>filt_u_v_ssh.nc contains horizontal velocity and sea surface height after Lagrangian filtering</p> <p>This work was supported by NASA award 80NSSC20K1142.</p>
Monthly SSH from SODA 2.2.4
<p>This is the monthly sea surface height from the Simple Ocean Data Assimilation (SODA, version 2.2.4) between 1948-2010 (Carton & Giese, 2008).</p> <p>Carton, J. A., & Giese, B. S. (2008). A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA). <em>Monthly Weather Review</em>, 136, 2999–3017. https://doi.org/10.1175/2007MWR1978.1</p>
Sea Surface Height (SSH) maps for the California Current System, Jan-May 2018
<p>This is a dataset in netCDF format comprising daily sea surface height maps in the California Current system during between Jan-May 2018, produced by AVISO and 2DVAR, described and analyzed in the research paper:</p> <p>Archer, M., Li, Z., & Fu, L.‐L. (2020). Increasing the space‐time resolution of mapped sea surface height from altimetry. <em>Journal of Geophysical Research: Oceans</em>, 125, e2019JC015878. <a href="https://doi.org/10.1029/2019JC015878">https://doi.org/10.1029/2019JC015878</a></p> <p>Please see metadata and/or paper for more details. </p>
SSH data set used for Rossby Wave Analysis, extraction from ORCA12.L46-MJM189 DRAKKAR simulation
<p>This data set corresponds to the Sea Surface Heigh (SSH) silmulated by the NEMO ocean circulation model, under the ORCA12.L46-MJM189 configuration, developped in the frame of the DRAKKAR project. This particular data set is an extraction from the native numerical grid, covering the area between 38N and 40N in the North Altantic ocean, for the period 1970 to 2015. The data are concatenated in a single file with 5-days average of SSH. The corresponding metrics for this sub domain are also present in this netcdf file. This subset was used in Watelet et al. (2020) submitted paper, dealing with Rossby waves analysis.</p>
SSH CENTRE - Mini-reports : Focus groups on "Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030"
<p>SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU's transition to carbon neutrality. </p><p>SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.</p><p>This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU's journey to a sustainable future.</p><p>The aim of the focus groups was to gather citizen's perspectives, their hopes, concerns and ideas related to the Horizon Mission of Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030. The focus group discussion topics while remaining close to the Mission, avoid specific technical references to allow citizens to contribute based on their differing levels of understanding. As part of the SSH CENTRE project, in total, four focus group series will be conducted relating to Adaptation to Climate Change; Restore our Ocean and Waters by 2030; 100 Climate-Neutral and Smart Cities by 2030; A Soil Deal for Europe. </p><p>Notes were taken during each focus groups and turned into mini-reports. These mini-reports sum up the essence of the discussion: the participants' main ideas and some interesting quotes. </p>
Machine Learning Assisted SSH Keys Extraction From The Heap Dump
<p>This dataset contains heap dump of OpenSSH that contains session keys.</p> <p>On the performance test data, we also include the PCAP file that contains the encrypted SSH network traffic. With the correct session keys, it can be decrypted.</p>
Daily climatology of 3D ocean currents, SST and SSH based on a 22 year run of the SEA-COFS model
<p>This dataset is a daily climatology created based on a 22 year free run of the SEA-COFS model full domain (EAC 25.25 - 45.55 °S) forced with BARRA-R winds and tides, spanning from January 1994 to September 2016. The model has 30 sigma-stretch vertical levels and an across-shore resolution of 2.5 km (on shelf) – 6 km (off shelf) and 5 km along-shore. For a full description of this version of the model and its validation refer to the following papers:</p> <ul> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2021). <a href="https://doi.org/10.1029/2021GL094115%20">Dynamics of interannual eddy kinetic energy modulations in a Western Boundary Current</a>. <em>Geophysical Research Letters,</em>Vol 48, October 2021.DOI: <a href="https://doi.org/10.1029/2021GL094115">https://doi.org/10.1029/2021GL094115</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2021/2021GL094115.pdf">[PDF file]</a></li> <li> </li> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2022). <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">Variability and Drivers of Ocean Temperature Extremes in a Warming Western Boundary Current</a>. <em>Journal Of Climate,</em> February 2022. DOI: <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">https://doi.org/10.1175/JCLI-D-21-0622.1</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2022/Li_2022.pdf">[PDF file]</a></li> </ul> <p>This daily climatology of 365 days was created with NCO tools by taking the mean of the 22 daily average ROMS output files (one for each year) to generate each climatological day. Specific commands used are recorded in the NetCDF file history. Leap year days were excluded since its climatology was computed with only 5 instances.</p> <p>A sample file with the climatological data for January 1st is provided here as an example of the NetCDF format of the dataset. The entire dataset is one file of approximately 62GB and can be provided upon request. </p> <p><strong>NOTE: </strong>The ocean_time variable reflects the dates of the year 1994, but the values of the variables correspond to climatological values computed as described. </p> <p>The ROMS variables below are present in this daily climatology, as well as the S-coordinate stretching curves, grid defining variables and other time independent parameters:</p> <p>AKs = "time-averaged salinity vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKt = "time-averaged temperature vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKv = "time-averaged vertical viscosity coefficient" [meter2 seconds-1]</p> <p>bustr = "time-averaged bottom u-momentum stress" [newton meter-2]</p> <p>bvstr = "time-averaged bottom v-momentum stress" [newton meter-2]</p> <p>omega = "time-averaged S-coordinate vertical momentum component" [meter3 second-1]</p> <p>pvorticity = "time-averaged potential vorticity" [meter-1 second-1]</p> <p>pvorticity = "time-averaged 2D potential vorticity" [meter-1 second-1]</p> <p>rho = "time-averaged density anomaly" [kilogram meter-3]</p> <p>rvorticity = "time-averaged relative vorticity, vertical component" [second-1]</p> <p>rvorticity_bar = "time-averaged 2D relative vorticity" [second-1]</p> <p>salt: = "time-averaged salinity" [PSU]</p> <p>shflux = "time-averaged surface net heat flux" [watt meter-2]</p> <p>ssflux = "time-averaged surface net salt flux, (E-P)*SALT" [meter second-1]</p> <p>sustr = "time-averaged surface u-momentum stress" [newton meter-2]</p> <p>svstr = "time-averaged surface v-momentum stress" [newton meter-2]</p> <p>temp = "time-averaged potential temperature" [Celsius]</p> <p>u = "time-averaged u-momentum component" [meter second-1]</p> <p>u_eastward = "time-averaged eastward momentum component at RHO-points" [meter second-1]</p> <p>ubar = "time-averaged vertically integrated u-momentum component" [meter second-1]</p> <p>ubar_eastward = "time-averaged eastward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>uu = "time-averaged u-momentum times u-momentum" [meter2 second-2]</p> <p>uv = "time-averaged u-momentum times v-momentum" [meter2 second-2]</p> <p>v = "time-averaged v-momentum component" [meter second-1]</p> <p>v_northward = "time-averaged northward momentum component at RHO-points" [meter second-1]</p> <p>vbar = "time-averaged vertically integrated v-momentum component" [meter second-1]</p> <p>vbar_northward = "time-averaged northward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>vv = "time-averaged v-momentum times v-momentum" [meter2 second-2]</p> <p>w = "time-averaged vertical momentum component" [meter second-1]</p> <p>zeta = "time-averaged free-surface" [meter]</p>
Dataset: Enabling SSH Protocol Visibility in Flow Monitoring
<p>Dataset contains SSH flows from Masaryk University campus network. Captured during August 2018. IP addresses are hashed using salted SHA-256.</p> <p>The header for both data files is as follows:</p> <pre><code>Date flow start|Date flow end|Src IP|sPort|Dst IP|dPort|Proto|Packets|Bytes|Flags|SSH Client Version|SSH Client Application|SSH Server Version|SSH Server Application|SSH Login Attempts|SSH Login|SSH Kex|SSH Host Key|SSH Client Encryption|SSH Server Encryption|SSH Client MAC|SSH Server MAC|SSH Client Compression|SSH Server Compression</code></pre> <p> </p> <p>When using this dataset, please cite the original work as follows:</p> <pre><code>@inproceedings{1519096, author = {Celeda, Pavel and Velan, Petr and Kral, Benjamin and Kozak, Ondrej}, address = {Washington DC, USA}, booktitle = {IFIP/IEEE International Symposium on Integrated Network Management (IM 2019)}, keywords = {SSH; flow; monitoring; network; dataset}, howpublished = {online}, language = {eng}, location = {Washington DC, USA}, isbn = {978-3-903176-15-7}, pages = {569-574}, publisher = {IFIP Open Digital Library}, title = {Enabling SSH Protocol Visibility in Flow Monitoring}, url = {http://dl.ifip.org/db/conf/im/im2019exp/189410.pdf}, year = {2019} }</code></pre> <p> </p>
SSH Username Enumeration Attack Detection Dataset
<p>The dataset is collected from a closed-environment network using network monitoring tools installed in the data collection point. The dataset generation was achieved through the use of common vulnerabilities and exposures (CVE) with the identification number CVE-2018-15473 retrieved from the public exploits database and pcap file of normal traffic obtained from public training repository. A total of 36,273 instances were collected with two classes <em>“username enumeration attack”</em> and “<em>non-username enumeration</em>”. We chose the terms <em>“username enumeration attack”</em> and “<em>non-username enumeration</em>” instead of the traditional <em>“attack”</em> and <em>“normal”</em> label notations since <em>“</em>normal<em>”</em> traffic data could contain attacks other than username enumeration attack.</p> <p>The username enumeration attack corresponds to the attack traffic while non-username enumeration traffic corresponds to the normal traffic. This traffic reflects different services including emails, DNS, HTTP, web, few to mention. Several data preprocessing techniques were carried out including categorical encoding. Both label encoding and one hot encoding techniques were used to transform categorical feature values into numerical feature values. Hence, two types of datasets were generated. </p>
The linked data ecosystem for SSH and a case study from the cultural heritage domain
<p>Semantic Web, an extension of the current web, focuses on the structure of data. The aim is to develop data structures that will be more effectively processable by computers, in contrast to the limited functionality of the contemporary scriptocentric web. Semantic Web, therefore, aims to transform the web into a global ‘database’ and lead the developments toward the Web of Data. The specifications developed or related to this aim, as well as the processes and rules for achieving it, are known as Linked Data, i.e. (inter)connected data from different sources. Linked Data creates a content-agnostic, as well as software-agnostic ecosystem, which can be used for encoding and publishing all types of information from various domains.</p> <p>The first presentation of this webinar introduces the basic concepts of Linked Data and shows that Linked Data are particularly suitable for Social Sciences and Humanities (SSH) since they do not emphasize metrics and quantifications but conceptualizations and reason. After presenting the paradigm shift introduced by linked data concerning the development of the web, a case study from the cultural heritage domain will be discussed.</p> <p>The second presentation showcases SearchCulture.gr, the Greek cross-domain Cultural Data Aggregator, as a state-of-the-art case, for the use of Linked data in the cultural domain. The National Documentation Centre of Greece (EKT) develops this service, which has collected a growing number of 800.000 digitized Cultural Heritage Objects (CHOs) from 73 cultural institutions. Moreover, it is the Accredited National Aggregator for Europeana having provided more than 580.000 CHOs so far. Addressing metadata heterogeneity has been a key target from the start. Controlled Linked Data vocabularies for item types, historical periods, subjects and persons have been developed over the course of the past years and are being used for the semantic enrichment of the CHOs’ metadata. This presentation addresses the challenges, methodology and tools used over the past 7 years for the process of enriching the aggregated CHOs’ metadata. This process classifies and disambiguates the aggregated data, provides multilinguality and adds significant browse and search functionalities to the portal and, therefore, opens new horizons for SSH research.</p>
About SSH CENTRE - partner perspective: Marianne Ryghaug
<p>Marianne Ryghaug from NTNU describes the SSH CENTRE project and what NTNU will bring to the project.</p>
About SSH CENTRE - partner perspective: Mojca Drevenšek
<p>Mojca Drevenšej from Consensus Communications describes the SSH CENTRE project and what her organisation will bring to the project.</p>
About SSH CENTRE - partner perspective: Imre Keseru
<p>Imre Keseru from Vrije Universiteit Brussels describes the SSH CENTRE project and what his organisation will bring to the project.</p>
About SSH CENTRE - partner perspective: Julia Leventon
<p>Julia Leventon from Global Change Research Institute describes the SSH CENTRE project and what her organisation will bring to the project.</p>
About SSH CENTRE - partner perspective: Alessandra Cardaci
<p>Citizens as the main focus of the activities is one of the core goals of Friends of Europe in our project. Know more about their role in our project by listening Alessandra Cardaci, project manager.</p>
About SSH CENTRE - partner perspective: Marta Arosio
<p>Marta Arosio from Energy Cities describes the SSH CENTRE project and what her organisation will bring to the project.</p>
About SSH CENTRE - partner perspective: Gergely Tagai
<p>Gergely Tagai from Institute for Regional Studies describes the SSH CENTRE project and what his organisation will bring to the project.</p>
About SSH CENTRE - partner perspective: Ganna Gladkykh
<p>Ganna Gladkykh from European Energy Research Alliance describes the SSH CENTRE project and what her organisation will bring to the project.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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