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190 results for “intrusions”
FIG. 6 in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 6. — Pointed rib of a large bovid, from the Neolithic collective burial at Abri des Autours: A, general view (from left to right: internal, centre and external views); B, detail of the tip (internal view) showing some polish; C, detail of the tip (internal view) showing fine perpendicular striations; D, detail of a lateral edge of the rib devoid of striations. Scale bars: A, 10 mm; B, 500 μm; C, D, 100 μm.
FIG. 3 in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 3. — Stratigraphy of Abri des Autours, east–west profile (modified from Cauwe 1994): 1a, b, clay, without pebbles; 2, as layer 6, but with more clay; 3a, brown, argillaceous deposit with small, densely dispersed pebbles; 3b, as layer 3a, but less humic. Contains the Neolithic multiple burial; 3c, as layer 3a, but strongly compacted by calcite precipitations; 4, clay with fine pebbles; 5a, b, cryoclastic deposit without humic or clayish fraction; layer 5b corresponds to very fine gravels, while layer 5a includes larger elements. The Mesolithic single burial extends through both of these layers; 6, brown-grey clayey deposit, strongly inclined. Contains the Mesolithic collective burial; 7, cryoclastic layer, included in clayey orange-brown sediment; 8, thin pebble deposit mixed with greyish sediment; 9, low wall constructed of unworked stone blocks.
Characterization of Functionalized Chromatographic Nanoporous Silica Materials by Coupling Water Adsorption and Intrusion with Nuclear Magnetic Resonance Relaxometry
<p>This data publication is based on the metadata and datasets underlying the manuscript "Characterization of Functionalized Chromatographic Nanoporous Silica Materials by Coupling Water Adsorption and Intrusion with Nuclear Magnetic Resonance Relaxometry" (<a href="https://doi.org/10.1021/acsanm.3c04330"><span>https://doi.org/10.1021/acsanm.3c04330</span></a>)</p> <p>Included are the datasets used, raw and processed data of Adsorption measurements (Water, Ar 87K, N2 77K), Water Intrusion measurements, NMR Relaxometry and solid state MAS NMR measurements. More information can be found in the Readme file.</p> <p> </p>
Local grafting heterogeneities control water intrusion and extrusion in nanopores
<p>Datasets and scripts used to generate data.</p>
Supplementary Material for "Intrusion Tolerance for Networked Systems Through Two-Level Feedback Control"
<h2>Supplementary material for the paper "Intrusion Tolerance for Networked Systems Through Two-Level Feedback Control" </h2><p>The paper is submitted to "International Conference on Dependable Systems and Networks, 2024". Author names withheld for double-blind reviewing.</p><ul><li>The file <strong>proofs_and_hyperparameters.pdf </strong>contains proofs of Theorem 1--2 and Corollary 1 in the paper. It also includes formulas for computing the belief state (Eq. 4) and for computing the curves in Fig. 6. It also includes a complete list of hyperparameters used for all experiments detailed in the paper.</li><li>The file <strong>ids_alerts_statistics.json</strong> contains the statistics used to produce Fig. 10 in the paper and to define the parameter Z for the experiments in section VIII.<ul><li>The JSON file contains a single object with the following keys: 'conditionals_counts', 'conditionals_kl_divergences', 'conditionals_probs', 'conditions', 'descr', 'emulation_name', 'id', 'initial_distributions_counts', 'initial_distributions_probs', 'initial_maxs', 'initial_means', 'initial_mins', 'initial_stds', 'maxs', 'means', 'metrics', 'mins', 'num_conditions', 'num_measurements', 'num_metrics', 'stds'. </li><li>The key "conditionals_counts" leads to another object with the following keys: 'A:CVE-2010-0426 exploit_D:Continue_M:[]', 'A:CVE-2015-3306 exploit_D:Continue_M:[]', 'A:CVE-2015-5602 exploit_D:Continue_M:[]', 'A:CVE-2016-10033 exploit_D:Continue_M:[]', 'A:Continue_D:Continue_M:[]', 'A:DVWA SQL Injection Exploit_D:Continue_M:[]', 'A:FTP dictionary attack for username=pw_D:Continue_M:[]', 'A:Ping Scan_D:Continue_M:[]', 'A:SSH dictionary attack for username=pw_D:Continue_M:[]', 'A:Sambacry Explolit_D:Continue_M:[]', 'A:ShellShock Explolit_D:Continue_M:[]', 'A:TCP SYN (Stealth) Scan_D:Continue_M:[]', 'A:Telnet dictionary attack for username=pw_D:Continue_M:[]', 'intrusion', 'no_intrusion'</li><li>The above keys correspond to different types of intrusions, see Table 6 in the paper.</li><li>Each of the keys listed above leads to a new object with 1551 keys which correspond to different types of metrics collected from the infrastructure. The metric used for produce Fig. 10 in the paper is called "alerts_weighted_by_priority". This key leads to another object where the keys correspond to the number of alerts weighted by priority and the values correspond to the measurements from the system.</li></ul></li><li>The file <strong>intrusion_traces.zip</strong> contains 6400 intrusion traces. Each trace contains a list of attacker actions and the corresponding measurements from the system. When unzipped, it is a directory with 64 files which take up 1500GB. Each file contains 100 traces in JSON format.</li><li>The file <strong>source_code_and_docker_files.zip </strong>contains the source code and the docker containers used for the experiments. It is a system we have developed for 3 years. It includes 225,000 lines of Python, 40,000 lines of JavaScript, 3000 lines of Dockerfiles, 2500 lines of Makefile, and 1800 lines of Bash. When unzipped one can find documentation about the source code in a file called "documentation.pdf" and in the README file.</li></ul>
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>
Arctic Moisture Intrusion Dataset v1 1/2 (1979-1999)
<p>Moisture intrusion tracking algorithm output (1/2) from 1979-1999. Output contains binary files with associated ID numbers of moisture intrusion events and assoicated ERA5 total column water vapor and northward water vapor flux. </p> <p>Dataset 2/2 - 10.5281/zenodo.13984122</p>
Model outputs of Wei et al. (2022): "Salt intrusion as a function of estuary length in periodically weakly stratified estuaries", published in Geophysical research Letters.
<p>The .mat file includes all model data used in the study "Salt intrusion as a function of estuary length in periodically weakly stratified estuaries", published in Geophyscial Research Letters, 2022. The .txt file contains description of all physical variables contained in the .mat file.</p>
Dataset for the paper "A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors"
<p>The state identification problem seeks to identify power usage patterns of any system, like buildings or factories, of interest. In this challenge paper, we make power usage dataset available from 8 institutions in manufacturing, education and medical institutions from the US and India, and an initial unsupervised machine learning based solution as a baseline for the community to accelerate research in this area.</p> <p>Additional data for more days (from January-August 2022) for the same locations presented in our paper can be requested for research purposes by contacting the authors.</p> <p>Our GitHub repository - https://github.com/ai4society/PowerIoT-State-Identification</p> <p>If you are using this data, please cite,</p> <blockquote> <pre>@inproceedings{midas-state-id, author = {Bharath C Muppasani and C J Anand and Chinmayi Appajigowda and Biplav Srivastava and Lokesh Johri}, title = {A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors}, booktitle = {Proc. Thirty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI-23)}, year = {2023}, keywords = {Signal Processing (eess.SP), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences}, copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International} }</pre> </blockquote>
Dataset: Intrusion Inc. (INTZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 1 in Invasions of two estuarine gobiid species interactively induced from water diversion and saltwater intrusion
Figure 1. The East Route of South-to-North Water Transfer Project, showing the five major lakes along the route (shadow areas) as storages, the Grand Canal as conveyance, and geographic relationships of the major rivers (i.e., the Yangtze River, the Huai River, and the Yellow River) with the route. The Nansi Lake is separated into the Lower Nansi Lake and Upper Nansi Lake by the Erji Dam. The year of the first record of the two invasive species, Taenioides cirratus and Tridentiger bifasciatus, in each of these lakes was indicated to show their invasion patterns.
Assessment of Hydrophilicity/Hydrophobicity in Mesoporous Silica by combining Adsorption, Liquid Intrusion and solid-state NMR spectroscopy
<p>This data publication is based on the metadata and datasets underlying the manuscript "</p> <p><span>Assessment of Hydrophilicity/Hydrophobicity in Mesoporous Silica by Combining Adsorption, Liquid Intrusion, and Solid-State NMR Spectroscopy (</span>"https://doi.org/10.1021/acs.langmuir.3c03516")</p> <p>Included are the datasets used, raw and processed data of Adsorption measurements (Water, Ar 87K), Water Intrusion measurements, solid state MAS NMR measurements. and molecular dynamics simulations. </p>
Multiscale Temporal Response of Salt Intrusion to Transient River and Ocean Forcing
<p>Model and validation data presented in Payo Payo et al, Multiscale Temporal Response of Salt Intrusion to Transient River and Ocean Forcing (JGR-Oceans, 2021, submitted).</p> <p>We used a 3-dimensional unstructured hydrodynamic model 3-dimensional unstructured hydrodynamic model (FVCOM Finite Volume Community Ocean Model)understand river and tide forcing to disentangle the multiscale temporal pattern of salt intrusion in estuaries subject to transient forcing due to river and tide. We implemented the Pearl River Delta as a case study for 2007/2008.</p> <p>The dataset consists of:</p> <p>·Measured and modelled salinity and elevation data at the validation stations.</p> <p>·River discharge data</p> <p>·SSH data</p> <p>·Salt intrusion length</p>
Dataset for: The barrier to radial oxygen loss protects roots against hydrogen sulphide intrusion and its toxic effect
<ul> <li> <span>The root barrier to radial O<sub>2</sub> loss (ROL) is a key root trait preventing </span><span>O<sub>2</sub></span><span> loss from roots to anoxic soils thereby enabling root growth into anoxic, flooded soils. </span> </li> <li> <span>We hypothesized that the ROL barrier can also prevent intrusion of hydrogen sulphide (H<sub>2</sub>S), a potent phytotoxin in flooded soils. Using H2S and </span><span>O<sub>2</sub></span><span>-sensitive microsensors, we measured the apparent permeance to </span><span>H<sub>2</sub>S</span><span> of rice roots and tested whether restricted </span><span>H<sub>2</sub>S</span><span> intrusion reduced its adverse effects on root respiration and if </span><span>H<sub>2</sub>S</span><span> could induce the formation of a ROL barrier.</span> </li> <li> <span>The ROL barrier reduced apparent permeance to </span><span>H<sub>2</sub>S</span><span> by almost 99%, greatly restricting </span><span>H<sub>2</sub>S</span><span> intrusion. The ROL barrier acted as a shield towards </span><span>H<sub>2</sub>S</span><span>; </span><span>O<sub>2</sub></span><span> consumption in roots with a ROL barrier remained unaffected at high </span><span>H<sub>2</sub>S</span><span> concentration (500 µM), compared to a 67% decline in roots without a barrier. Importantly, low </span><span>H<sub>2</sub>S</span><span> concentrations induced the formation of a ROL barrier.</span> </li> <li> <span>In conclusion, the ROL barrier plays a key role in protecting against </span><span>H<sub>2</sub>S</span><span> intrusion, and </span><span>H<sub>2</sub>S</span><span> can act as an environmental signalling molecule for the induction of the barrier. The study demonstrates the multiple functions of the suberized/lignified outer part of the rice root beyond that of restricting ROL.</span> </li> </ul>
Supplementary Datasets for Focused mid-crustal magma intrusion during continental break-up in Ethiopia'
<p>Supplementary Datasets for Focused mid-crustal magma intrusion during continental break-up in Ethiopia', including geochemical analyses (standards, secondary standards, and data)</p> <p>Dataset S1: Full dataset of all standards, secondary standards, and data.<br> Dataset S2: High resolution calibrated transmitted and reflected light microscope images of analysed melt inclusions.</p>
Data for: Wall fracturing versus mechanical instability as competing intrusion mechanisms of dikes: Insights from laboratory experiments
<p class="MsoNormal"><span>Igneous dike intrusion is a primary crust-forming process. Understanding its governing mechanism is very crucial for studies related to the lithosphere. We performed liquid injection experiments in the laboratory with two new crust analog model materials, i) ultrasound transmission gel (<em>USTG</em>) and gel wax. To conduct a properly scaled model experiment, we test their rheology using an <em>Anton Paar M302e</em> rheometer. The measured rheological data were presented in this present data repository. We identified three mechanisms from our laboratory studies: a) fracturing, b) interfacial instability, and c) hybrid, i.e., a combination of both. These three mechanisms give rise to distinct 3D geometries. To quantitatively analyze their geometric shapes, we performed fractal, aspect ratio, and skewness-kurtosis analysis. The procedure and the data collected during the analysis were also presented in the current data repository. </span></p>
Group intrusions by a brood parasitic fish are competitive not cooperative
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Data for: Wall fracturing versus mechanical instability as competing intrusion mechanisms of dikes: Insights from laboratory experiments
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Dataset for: The barrier to radial oxygen loss protects roots against hydrogen sulphide intrusion and its toxic effect
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No evidence for a role of trills in male response to territorial intrusion in a complex singer, the Thrush Nightingale
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