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14 results for “key learnings”
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>
Extracting Session Keys From the Main Memory Using Brute-force and Machine Learning
<p>This dataset contains:</p> <ol> <li>Heap dump of three version of OpenSSH (V_7_9_P1, V_8_0_P1 and V_8_1_P1)</li> <li>Heap dump of two applications that uses TLS (lynx and curl)</li> <li>Network recording in format of pcap</li> <li>JSON file that contains the keys' information</li> </ol> <p>The source code is available at: https://github.com/smartvmi/SSH-TLS-key-extraction</p>
Figure 7. Comparison for identifying the success factor key versus the learning approach-Micro Learning: A Modernized Education System
<p>There are many unique activities involved in microlearning, which provides a high rate of successful knowledge transformation. To the inquiry made in research on the activities dealing with microlearning that support knowledge transformation, figure 6 shows that mind mapping has the highest view of respondents, up to 72%, the nearest to it is story telling with 50%. The variation displayed in the figure is significant. 44% of the respondents opt for tagging followed by text with 38%.</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>
Data from: Cornerstones are the key stones: Using interpretable machine learning to probe the clogging process in 2D granular hoppers
Open the record for dataset details and reuse information.
Datasets from the study Peer learning as a key component of an Integrated Teaching Method in large size classes
<p>The theory and research on learning in higher education are pointing out that active methods are able to improve learners’ performance more than passive ones and to trigger situated and deep learning. However, some factors prevent their spread and trust amongst university instructors, like their complex logistics in large size lectures. Indeed, in academic crowded classes these interactive-engagement methodologies are sporadically employed and seem to be rarely successful. In this context, in the academic years 2017-2018 and 2018-2019 we carried out a quasi-experiment at Politecnico di Milano aimed at investigating the effectiveness of the integration of peer learning activities, strengthened by the use of technology, into traditional Physics lectures as a teaching method in large size classes. Data related to both an experimental and a control group were gathered and analysed through descriptive and inferential statistics; Shapiro-Wilk, Levene, Mann-Whitney U, Wilcoxon signed-rank and Kruskal-Wallis tests were employed. Our combined strategy appears to be effective with relation to learning Physics and more successful than traditional courses centred on academic lectures, regardless of the difficulty of the issues investigated. Furthermore, a threshold for the exposure to peer learning in order for it to be effective has been highlighed.</p> <p>In this page it is shared:</p> <p>1) Dataset with reference to both 2017-2018 and 2018-2019</p> <p>2) R Script with reference to both 2017-2018 and 2018-2019</p>
Machine learning suggests small size is a key determinant of plasmid host range
<p>Plasmids mediate gene exchange across taxonomic barriers through conjugation, shaping bacterial evolution for billions of years. While plasmid mobility can be harnessed for genetic engineering and drug-delivery applications, rapid plasmid-mediated spread of resistance genes has rendered most clinical antibiotics useless, posing an existential threat to human society. To solve this urgent problem, we must understand how plasmids spread across bacterial communities. Here, we applied machine-learning models to identify features that determine plasmid host range. We assembled an up-to-date dataset of more than thirty thousand bacterial plasmids, separated them into 1125 clusters, and assigned a distribution possibility score, which takes host distribution of each taxonomic rank and the sampling bias of the existing sequencing data into account, for each cluster. Using this score and an optimized plasmid feature pool, we built a model stack consisting of DecisionTreeRegressor, EvoTreeRegressor, and LGBMRegressor as base models and LinearRegressor as a meta-learner. Our analysis reveals that a short sequence length is most important for successful plasmid spread, followed by P-loop NTPases, mobility factors, and <span>β</span>-lactamases. Ours and other recent results suggest that small plasmids broaden their range by evading host defenses and using alternative modes of transfer instead of autonomous conjugation.</p>
Screening of key risk SNPs for glioma based on machine learning algorithms
<p>Glioma is a common primary malignant brain tumor and is the most aggressive and lethal solid tumor, accounting for approximately 80% of all intracranial malignancies. Our aim was to screen key SNP by LASSO regression and random forest (a machine learning algorithm) and construct a model based on these SNP to predict the risk of glioma in Chinese Han population.</p>
Evidence for maintenance of key components of vocal learning in aging budgerigars despite diminished affiliative social interaction
<p>In some species, the ability to acquire new vocalizations persists into adulthood and may be an important mediator of social interactions. While it is generally assumed that vocal learning persists undiminished throughout the lifespan of these open-ended learners, the stability of this trait remains largely unexplored. We hypothesize that vocal learning exhibits senescence, as is typical of complex cognitive traits, and that this decline may relate to age-dependent changes in sociality. The budgerigar (<em>Melopsittacus undulatus</em>), an open-ended learner which develops new contact call types that are shared with social associates upon joining new flocks, provides a robust assay for measuring the effects of aging on vocal learning ability. We formed captive flocks of 4 previously unfamiliar adult males of the same age class, either "young adults" (6 mo.-1 yr.) or "older adults" (≥ 3 yr.), and concurrently tracked changes in contact call structure and social interactions over time. Older adults exhibited decreased vocal diversity, which may be related to the sparser and weaker affiliative bonds observed in older adults. Older adults, however, displayed equivalent levels of vocal plasticity and vocal convergence compared to young adults, suggesting vocal learning ability is largely maintained into later adulthood in an open-ended learner.</p>
Immunoglobulin, Diagnosis, Evaluation, and Key Learnings Patient Registry (IDEaL™)
ClinicalTrials.gov study NCT01109810. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evidence for maintenance of key components of vocal learning in aging budgerigars despite diminished affiliative social interaction
Open the record for dataset details and reuse information.
Identification of Key Genes for seed germination of Astragalus Mongolicus using WGCNA and machine Learning methods
Open the record for dataset details and reuse information.
With the Development of Research, New Algorithms and Technologies Have Emerged, One of Which is Machine Learning. Machine Learning Can Extract Key Factors From Vast Amounts of Data, Identify Underlyin
ClinicalTrials.gov study NCT07121309. IPD Sharing: NO. Countries: 1. Publications: 0.
Identifying Key Attributes and Learning From a Local Falls Prevention Ambassador Programme
ClinicalTrials.gov study NCT06946303. IPD Sharing: NO. Countries: 1. Publications: 0.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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