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31 results for “Cybersecurity”
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>
Dataset: Reinforcing Cybersecurity Hands-on Training With Adaptive Learning
<p>This repository contains supplementary materials for the following conference paper:<br> <br> Pavel Seda, Jan Vykopal, Valdemar Švábenský, Pavel Čeleda.<em><br> Reinforcing Cybersecurity Hands-on Training With Adaptive Learning. </em><br> In Proceedings of the 51st IEEE Frontiers in Education Conference (FIE 2021).<br> <a href="https://doi.org/10.1109/FIE49875.2021.9637252">https://doi.org/10.1109/FIE49875.2021.9637252</a><br> <br> Preprint available at: <a href="https://arxiv.org/abs/2201.01574">https://arxiv.org/abs/2201.01574</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <p>Some of the linked repositories have their separate citation entry; please use that one as well, if possible.</p> <pre><code>@inproceedings{Seda2021reinforcing, author = {Seda, Pavel and Vykopal, Jan and \v{S}v\'{a}bensk\'{y}, Valdemar and \v{C}eleda, Pavel}, title = {{Reinforcing Cybersecurity Hands-on Training With Adaptive Learning}}, booktitle = {Proceedings of the 51st IEEE Frontiers in Education Conference}, series = {FIE '21}, location = {Lincoln, NE, USA}, publisher = {IEEE}, address = {New York, NY, USA}, month = {10}, year = {2021}, pages = {1--9}, numpages = {9}, isbn = {978-1-6654-3851-3}, url = {https://doi.org/10.1109/FIE49875.2021.9637252}, doi = {10.1109/FIE49875.2021.9637252}, }</code></pre> <p> </p>
Locked Shields Partners Run 23 (LSPR23): A novel IDS dataset from the largest live-fire cybersecurity exercise
<p>IDS Dataset from the Largest Live Fire Cybersecurity Exercise Using Virtual Blue Team Network Traffic.<br><br></p> <ul> <li> <p>LSPR23 is derived from Locked Shields 2023, a major live-fire cyber defense exercise.</p> </li> <li> <p>LSPR23 includes ~16M network flows, of which ~1.6M are labeled malicious.</p> </li> </ul> <p> </p> <p>Please cite our research article:"LSPR23: A novel IDS dataset from the largest live-fire cybersecurity exercise" when using our dataset:<br>https://doi.org/10.1016/j.jisa.2024.103847<br><br><br></p>
Dataset: Shell Commands Used by Participants of Hands-on Cybersecurity Training
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Pavel Seda, Pavel Čeleda.<br> <em>Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training.</em><br> In Elsevier Data in Brief. 2021.<br> <a href="https://doi.org/10.1016/j.dib.2021.107398">https://doi.org/10.1016/j.dib.2021.107398</a></p> <ul> </ul> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2021dataset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Seda, Pavel and \v{C}eleda, Pavel}, title = {{Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training}}, journal = {Data in Brief}, publisher = {Elsevier}, volume = {38}, year = {2021}, issn = {2352-3409}, url = {https://doi.org/10.1016/j.dib.2021.107398}, doi = {10.1016/j.dib.2021.107398}, }</code></pre> <p>The data were collected using a logging toolset referenced <a href="https://zenodo.org/record/5126693">here</a>.</p> <p><strong>Attached content</strong></p> <ol> <li><strong>Dataset (data.zip).</strong> The collected data are attached here on Zenodo. A copy is also available in <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/datasets/commands">this repository</a>.</li> <li><strong>Analytical tools (toolset.zip).</strong> To analyze the data, you can instantiate the toolset or <a href="https://gitlab.ics.muni.cz/muni-kypo/tools/commands-elk">this project for ELK</a>.</li> </ol> <p><strong>Version history</strong></p> <ul> <li>Version 1 (<a href="https://zenodo.org/record/5137355">https://zenodo.org/record/5137355</a>) contains 13446 log records from 175 trainees. These data are precisely those that are described in the associated journal paper. Version 1 provides a snapshot of the state when the article was published.</li> <li>Version 2 (<a href="https://zenodo.org/record/5517479">https://zenodo.org/record/5517479</a>) contains 13446 log records from 175 trainees. The data are unchanged from Version 1, but the analytical toolset includes a minor fix.</li> <li>Version 3 (<a href="https://zenodo.org/record/6670113">https://zenodo.org/record/6670113</a>) contains 21762 log records from 275 trainees. It is a superset of Version 2, with newly collected data added to the dataset.</li> <li>The current Version 4 (<a href="https://zenodo.org/record/8136017">https://zenodo.org/record/8136017</a>) contains 21459 log records from 275 trainees. Compared to Version 3, we cleaned 303 invalid/duplicate command records.</li> </ul>
Dataset: Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Pavel Čeleda, Jan Vykopal, Silvia Brišáková.<br> <em>Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges.</em><br> In Elsevier Computers & Security. 2020.<br> <a href="https://doi.org/10.1016/j.cose.2020.102154">https://doi.org/10.1016/j.cose.2020.102154</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2101.01421">https://arxiv.org/abs/2101.01421</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2020cybersecurity, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and \v{C}eleda, Pavel and Vykopal, Jan and Bri\v{s}\'{a}kov\'{a}, Silvia}, title = {{Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges}}, journal = {Computers \& Security}, publisher = {Elsevier}, volume = {102}, year = {2020}, issn = {0167-4048}, url = {https://www.sciencedirect.com/science/article/pii/S0167404820304272}, doi = {10.1016/j.cose.2020.102154}, }</code></pre> <p><strong>Attached content</strong></p> <p>See the README.md file inside the attached ZIP file for more details.</p>
Dataset: Behavior of Participants in Hands-on Cybersecurity Training Suitable for Process Mining
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Radek Ošlejšek, Martin Macák, Karolína Dočkalová Burská.<br><em>Hands-on cybersecurity training behavior data for process mining.</em><br>In Elsevier Data in Brief. 2023.<br>Available as open-access article on <a href="https://doi.org/10.1016/j.dib.2023.109956">https://doi.org/10.1016/j.dib.2023.109956</a></p> <p><strong>Contents</strong></p> <p>Datasets store event logs of trainees participating in hands-on cybersecurity exercises organized in the <a href="https://www.kypo.cz">KYPO Cyber Range</a>. The data includes training scenarios (expected behavior), raw event logs in the JSON format, and aggregated behavioral data suitable for process mining analysis.</p> <ol> <li><strong>Data1:</strong> A dataset of 52 trainees participating in the <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> exercise adapted an insider attack scenario. No time restrictions were posed on playtime. The data file is structured as follows: <ul> <li>training_definition.json: The exercise content – cybersecurity tasks and hints. The training is based on the <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> game adapted to an insider attack scenario.</li> <li>training_events: Recorded progress of trainees within the exercise, i.e., the status of completing tasks.</li> <li>command_histories: Recorded commands executed on network hosts.</li> <li>process_mining.csv: Complete PM-ready dataset suitable for process discovery or conformance analysis.</li> <li>process_mining_simplified.csv : Reduced PM-ready dataset with semantically identical events being removed.</li> </ul> </li> <li><strong>Data2:</strong> A dataset of 48 trainees participating in the original <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> exercise. Three supervised training sessions were restricted to two hours of playtime. The structure follows the structure of Data1.</li> <li><strong>Tool:</strong> A Java application used to aggregate raw JSON data and transform them into a CSV format suitable for process mining techniques.</li> </ol> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original work.</p> <pre><code>@article{Oslejsek2023dataset, author = {Radek O\v{s}lej\v{s}ek and Martin Mac\'{a}k and Karol\'{i}na {Do\v{c}kalov\'{a} Bursk\'{a}}}, title = {Hands-on cybersecurity training behavior data for process mining}, journal = {{Data in Brief}}, publisher = {Elsevier}, issn = {2352-3409}, year = {2023}, volume = {52}, doi = {10.1016/j.dib.2023.109956}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923009873} }</code></pre>
Dataset: Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises
<p>This repository contains supplementary materials for the following conference paper:</p> <p>V. Švábenský, R. Weiss, J. Cook, J. Vykopal, P. Čeleda, J. Mache, R. Chudovský, A. Chattopadhyay.<br> <em>Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises.</em><br> In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (SIGCSE 2022).<br> <a href="https://doi.org/10.1145/3478431.3499414">https://doi.org/10.1145/3478431.3499414</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2112.02053">https://arxiv.org/abs/2112.02053</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@inproceedings{Svabensky2022evaluating, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Weiss, Richard and Cook, Jack and Vykopal, Jan and \v{C}eleda, Pavel and Mache, Jens and Chudovský, Radoslav and Chattopadhyay, Ankur}, title = {{Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises}}, booktitle = {Proceedings of the 53rd ACM Technical Symposium on Computer Science Education}, series = {SIGCSE '22}, location = {Providence, RI, USA}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, month = {03}, year = {2022}, pages = {787--793}, numpages = {7}, isbn = {978-1-4503-9070-5}, url = {https://doi.org/10.1145/3478431.3499414}, doi = {10.1145/3478431.3499414}, }</code></pre> <p><strong>Attached content</strong></p> <p>The materials include the research dataset, source code, and graphs. See the README.md file inside the attached ZIP file for more details.</p>
Dataset: Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Pavel Čeleda, Lydia Kraus.<br> <em>Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training.</em><br> In Springer Education and Information Technologies. 2022.<br> <a href="https://doi.org/10.1007/s10639-022-11093-6">https://doi.org/10.1007/s10639-022-11093-6</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2307.08582">https://arxiv.org/abs/2307.08582</a></p> <ul> </ul> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2022applications, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and \v{C}eleda, Pavel and Kraus, Lydia}, title = {{Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training}}, journal = {Education and Information Technologies}, publisher = {Springer}, volume = {27}, year = {2022}, issn = {1360-2357}, url = {https://doi.org/10.1007/s10639-022-11093-6}, doi = {10.1007/s10639-022-11093-6}, }</code></pre> <p><strong>Attached content</strong></p> <p>The files included in the ZIP archive are:</p> <ul> <li>`All-discovered-papers.bib` -- a BibTeX export of the Mendeley database of all considered papers discovered by the automated search.</li> <li>`Candidate-papers-reviewer1.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the first investigator.</li> <li>`Candidate-papers-reviewer2.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the second investigator.</li> <li>`Selected-papers.bib` -- a BibTeX export of the Mendeley database of the 35 papers selected for the literature review.</li> <li>`Selected-papers.xlsx` -- an Excel spreadsheet with the extracted information about the selected papers.</li> <li>`Selected-papers.csv` -- a CSV equivalent of the Excel spreadsheet.</li> </ul>
Dataset: First Trust NASDAQ Cybersecurity ETF (CIBR) 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.
Dataset: Global X Cybersecurity ETF (BUG) 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.
Dataset: WisdomTree Cybersecurity Fund (WCBR) 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.
Dataset: ProShares Ultra Nasdaq Cybersecurity (UCYB) 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.
Dataset: Themes Cybersecurity ETF (SPAM) 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.
Dataset: Xtrackers Cybersecurity Select Equity ETF (PSWD) 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.
Dataset: What Are Cybersecurity Education Papers About? A Systematic Literature Review of SIGCSE and ITiCSE Conferences
<p>This repository contains supplementary materials for the following conference paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Pavel Čeleda.<br> <em>What Are Cybersecurity Education Papers About? A Systematic Literature Review of SIGCSE and ITiCSE Conferences.</em><br> In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE 2020).<br> <a href="https://doi.org/10.1145/3328778.3366816">https://doi.org/10.1145/3328778.3366816</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/1911.11675">https://arxiv.org/abs/1911.11675</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code class="language-json">@inproceedings{Svabensky2020what, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and \v{C}eleda, Pavel}, title = {{What Are Cybersecurity Education Papers About? A Systematic Literature Review of SIGCSE and ITiCSE Conferences}}, booktitle = {Proceedings of the 51st ACM Technical Symposium on Computer Science Education}, series = {SIGCSE '20}, location = {Portland, OR, USA}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, month = {03}, year = {2020}, pages = {2--8}, numpages = {7}, isbn = {978-1-4503-6793-6}, url = {https://doi.org/10.1145/3328778.3366816}, doi = {10.1145/3328778.3366816}, }</code></pre> <p><strong>Attached content</strong></p> <p>The file "SIGCSE 2020 Literature Review.xlsx" is an Excel spreadsheet with three sheets corresponding to 1) all papers found by automated search, 2) manually excluded papers, and 3) papers included in the literature review. There are also three CSV files that correspond to the three individual sheets.</p>
Coursera - Cybersecurity in Healthcare (Hospitals & Care Centres)
<p><strong>Coursera - Cybersecurity in Healthcare (Hospitals & Care Centres)</strong></p> <p>M. Jofre</p> <p>The course "Cybersecurity in Healthcare" has been developed to raise awareness and understanding the role of cybersecurity in healthcare (e.g., hospitals, care centres, clinics, other medical or social care institutions and service organisations) and the challenges that surround it. In this course, we will cover both theoretical and practical aspects of cybersecurity. We look at both social aspects as technical aspects that come into play. Furthermore, we offer helpful resources that cover different aspects of cybersecurity. Even if you are not active in the healthcare domain, you will find helpful tips and insights to deal with cybersecurity challenges within any other organisation or in personal contexts as well.</p> <p>This course begins by introducing the opportunities and challenges that digitalisation of healthcare services has created. It explains how the rise of technologies and proliferation of (medical) data has become an attractive target to cybercriminals, which is essential in understanding why adequate cybersecurity measures are critical within the healthcare environment. In later modules, course contents cover the threats, both inside and outside of healthcare organisations like e.g. social engineering and hacking. Module 4 on Cyber Hygiene describes how to improve cybersecurity within healthcare organisations in practical ways. Module 5 looks deeper into how organisational culture affects cybersecurity, the cybersecurity culture, focusing on the interaction between human behaviour and technology and how organisational factors can boost or diminish the level and attention to cybersecurity in healthcare.</p> <p>References:</p> <p>[1] M. Jofre, “Holistic View Of Healthcare Cybersecurity Ecosystem,” Zenodo, Jul. 2020. doi: 10.5281/zenodo.7999970.<br> [2] M. Jofre, “Minimum Quality Standard For Cybersecurity Training In Healthcare,” Zenodo, Jun. 2020. doi: 10.5281/zenodo.8000029.<br> [3] M. Jofre et al., “Cybersecurity and Privacy Risk Assessment of Point-of-Care Systems in Healthcare—A Use Case Approach,” Appl. Sci., vol. 11, no. 15, Art. no. 15, Jan. 2021, doi: 10.3390/app11156699.</p>
SAUUHUPP Based Innovations in Network Computing and Cybersecurity
<p>Dear Zenodo Visitor,</p> <p> </p> <p>Welcome to the SAUUHUPP Innovations in Network Computing and Cybersecurity repository. This repository is dedicated to pioneering technologies grounded in the SAUUHUPP framework, a transformative approach for enhancing adaptability, security, and efficiency in network computing and cybersecurity.</p> <p> </p> <p>The innovations presented here explore how SAUUHUPP principles—focusing on scalable adaptability, universal harmony, and purposeful patterning—can be applied to the design and optimization of advanced networking and cybersecurity solutions. These solutions include dynamic, decentralized protocols for robust network connectivity, story encryption as an enhanced data protection technique, and VPNs that utilize the SAUUHUPP model to provide secure, context-aware communication channels.</p> <p> </p> <p>Within this repository, you’ll find technical papers, prototype designs, and implementation guides for:</p> <p> </p> <p>• Adaptive Wireless Mesh Protocol (AWMP), a scalable, energy-efficient alternative to Ethernet that combines Software-Defined Radio (SDR) and decentralized mesh networking.</p> <p>• Story Encryption methods, which transform data into narrative-driven encryption models, leveraging SAUUHUPP to integrate story logic, making unauthorized decryption significantly more complex and intuitive to monitor.</p> <p>• SAUUHUPP-based VPNs that adapt to network conditions and user contexts to provide seamless, resilient, and secure access across diverse environments, improving upon traditional VPN structures.</p> <p> </p> <p>We hope these resources inspire new directions in network computing and security. Whether you’re a developer, researcher, or enthusiast, we invite you to explore, contribute, and collaborate with us as we continue advancing these technologies.</p> <p> </p> <p>Thank you for your interest and support in pushing the boundaries of network computing with SAUUHUPP. Together, we aim to create adaptable, secure, and universally harmonious systems for the next era of technology.</p> <p> </p> <p>Warm regards,</p> <p>Prudencio L. Mendez</p> <p> SAUUHUPP Innovations Team</p>
Eight Reasons to Prioritize Brain-Computer Interface Cybersecurity
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Dataset: Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training
<p>This repository contains supplementary materials for the following conference paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Daniel Tovarňák, Pavel Čeleda.<br> <em>Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training.</em><br> In Proceedings of the 51st IEEE Frontiers in Education Conference (FIE 2021).<br> <a href="https://doi.org/10.1109/FIE49875.2021.9637052">https://doi.org/10.1109/FIE49875.2021.9637052</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2112.11118">https://arxiv.org/abs/2112.11118</a></p> <ol> </ol> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <p>Some of the linked repositories have their separate citation entry; please use that one as well, if possible.</p> <pre><code>@inproceedings{Svabensky2021toolset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Tovar\v{n}\'{a}k, Daniel and \v{C}eleda, Pavel}, title = {{Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training}}, booktitle = {Proceedings of the 51st IEEE Frontiers in Education Conference}, series = {FIE '21}, location = {Lincoln, NE, USA}, publisher = {IEEE}, address = {New York, NY, USA}, month = {10}, year = {2021}, pages = {1--9}, numpages = {9}, isbn = {978-1-6654-3851-3}, url = {https://doi.org/10.1109/FIE49875.2021.9637052}, doi = {10.1109/FIE49875.2021.9637052}, }</code></pre> <p><strong>Structure of the repository</strong></p> <p>We share four types of content described below. Each of the four types of materials includes:</p> <ul> <li>a link to an up-to-date GitLab repository, which may contain possible future revisions and error corrections, and</li> <li>a ZIP archive here on Zenodo that serves as a snapshot of the state when the article was published.</li> </ul> <p><strong>Attached content</strong></p> <ol> <li><strong>Logging toolset.</strong> It is implemented in the form of <a href="https://www.ansible.com/">Ansible</a> roles and consists of three separate projects: for <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-bash">Bash logging</a>, <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-msf">Metasploit logging</a>, and <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-forward">log forwarding within the sandbox</a>.</li> <li><strong>Sample training.</strong> To quickly test the toolset, instantiate the exemplary cybersecurity game called <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/junior-hacker">Junior hacker training</a>. It already deploys the logging; no further setup is needed.</li> <li><strong>Dataset.</strong> The data collected with the toolset are available at <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/datasets/commands">this repository</a>, which is being gradually updated with new data. Attached is a subset of this repository that includes the data analyzed in the paper.</li> <li><strong>Analytical tools.</strong> To analyze the data, you can either use the attached Python scripts, or instantiate <a href="https://gitlab.ics.muni.cz/muni-kypo/tools/commands-elk">this project for ELK</a>.</li> </ol>
An Overview of Cybersecurity in Zimbabwe's Financial Services Sector
<p>This data set captures the state of cybersecurity in the financial services sector in Zimbabwe. The study aims to assess the state of cybersecurity in a developing country to raise awareness and compliance and fight cybercrime. </p>
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