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4 results for “smart containers”
Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning
<p>README<br>Title<br>Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning</p> <p>Authors<br>Mays Abukeshek, School of Computer Science, Faculty of Technology, University of Sunderland, University of Huddersfield, UK<br>Email: mays.abukeshek@sunderland.ac.uk, Mays.abukeshek@hud.ac.uk<br>Basel Barakat, School of Computer Science, Faculty of Technology, University of Sunderland, UK<br>Email: basel.barakat@sunderland.ac.uk<br>Bamidele Ajayi, School of Computer Science, Faculty of Technology, University of Sunderland, UK<br>Email: bamidele.ajayi@research.sunderland.ac.uk<br>Abstract<br>This dataset supports the paper "Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning," which presents a comprehensive implementation of a cybersecurity solution for smart grid network containers. The methodology utilizes:</p> <p>Qualys API-based vulnerability scanning and reporting system for vulnerability identification<br>Docker deployment for security and isolation<br>Advanced load balancing techniques for resource optimization<br>Machine learning-powered anomaly detection for threat identification and vulnerability prioritization.<br>The dataset contains details of several simulated attacks enabling effective training and evaluation of a robust machine-learning model.</p> <p>Data Description<br>The dataset includes logs from conducted attacks on containerized nodes, generated to reflect real-world scenarios. The simulated attacks include:</p> <p>Denial of Service (DoS)<br>Remote-to-Local (R2L)<br>User-to-Root (U2R)<br>Probes<br>Contents<br>Csv_file.csv: This file contains the dataset used for training and evaluating the machine learning models. The columns in the dataset represent various features and results of the simulated attacks.<br>Data Columns and Rows<br>Timestamp:</p> <p>Description: The exact date and time when the data was recorded.<br>time: 2023-06-01 12:00:00</p> <p>Attack_Type:</p> <p>Description: The type of cyber-attack conducted.<br>Possible Values: DoS, R2L, U2R, Probe<br>Example: DoS<br>Notes: Categorizes the type of attack, crucial for training classification models.<br>CPU_Utilization (%):</p> <p>Description: The percentage of CPU resources used during the attack.<br>Example: 52.3<br>Notes: Indicates the load on the CPU during the attack, useful for assessing the impact of attacks on system performance.<br>Memory_Utilization (%):</p> <p>Description: The percentage of memory resources used during the attack.<br>Example: 63.4<br>Notes: Shows memory usage which can be a critical factor in understanding system performance under attack conditions.<br>Network_Bandwidth (Mbps):</p> <p>Description: The bandwidth of the network in Megabits per second.<br>Example: 100<br>Notes: Reflects the network load and is essential for analyzing the impact on network performance.<br>Vulnerabilities_Detected:</p> <p>Description: The number of vulnerabilities detected during the attack.<br>Example: 289<br>Notes: Indicates the effectiveness of the vulnerability scanning process and the system's exposure to threats.<br>Mean_Response_Time (ms):</p> <p>Description: The average response time in milliseconds during the attack.<br>Example: 87<br>Notes: Important for evaluating the responsiveness of the system under attack conditions.<br>Throughput (requests/second):</p> <p>Description: The number of requests the system can handle per second during the attack.<br>Example: 1068<br>Notes: Measures the capacity and efficiency of the system under load.<br>Example Row<br>Timestamp Attack_Type CPU_Utilization (%) Memory_Utilization (%) Network_Bandwidth (Mbps) Vulnerabilities_Detected Mean_Response_Time (ms) Throughput (requests/second)<br>2023-06-01 12:00:00 DoS 52.3 63.4 100 289 87 1068<br>Usage<br>This dataset can be used to:</p> <p>Train and evaluate machine learning models for cybersecurity applications in smart grid systems.<br>Analyze the performance of different machine learning models in detecting and prioritizing vulnerabilities.<br>Understand the impact of various types of cyber-attacks on containerized environments.<br>Methodology<br>The dataset was created using a combination of Qualys API-based vulnerability scanning and Docker containerization. Multiple container clusters were subjected to various simulated attacks, and the performance of machine learning models was evaluated based on accuracy, precision, recall, and F1-scores.</p> <p>Acknowledgments<br>This research was supported by the University of Sunderland and the University of Huddersfield.</p> <p>References<br>Please refer to the full paper for detailed methodology, implementation, and analysis:<br>IEEE</p>
Implementation of a learning healthcare system for Sickle Cell disease: List of smart data elements contained in the Epic Smartform and their SmartData types
Open the record for dataset details and reuse information.
SMaRT lncRNA controls translation of a G-quadruplex containing mRNA antagonizing the DHX36 helicase
GEO Series GSE128486. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing; Other.
The CONTEXT Dataset containing Contextual Faults of a Smart Factory
<p>Here you download the CONTEXT Dataset containing Contextual Faults of a Smart Factory. Our work as part of the Proceedings of the International Conference on Industry 4.0 and Smart Manufacturing is now published in Procedia Computer Science (Elsevier). <strong>If you refer to or use this dataset, cite this publication:</strong></p> <p><strong>Kaupp, Lukas; Webert, Heiko; Nazemi, Kawa; Humm, Bernhard; Simons, Stephan (2021): CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory. In: Procedia Computer Science 180, S. 492–501. DOI: 10.1016/j.procs.2021.01.265. </strong></p> <p>The dataset contains contextual faults recorded in the smart factory of the Darmstadt University of Applied Sciences. Each recording (CSV format) consists of an OPC-UA log file and log files of the corresponding machinery measured by our developed sensing units. Foldernames reflect experiment structure.</p> <p><date>-<run>_<count of build relays>_<g(ood/ok)/n(ot/failure)>_<experiment name></p> <p>You can find a detailed description of the experiments in our publication.</p> <p><strong>Description Update for a better data assessment.</strong></p> <p>OPC-UA Hierarchy - Factory Mapping:</p> <ul> <li>'Station10' - Management Station (send production instructions / receive production updates)[not physically involved in the production process]</li> <li>'Station 40 - Hochregallager' - High-Bay Storage Station</li> <li>'Station 50 - Roboter' - Robot Station</li> <li>'Station 60 - Presse' - Press Station</li> <li>'St20 SoftSPS' - Optical & Weight Inspection Station</li> <li>'Station30elPruefung' - Electrical Inspection Station</li> </ul>
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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.
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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.