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3 results for “Attack Graph”
Reproduction Package for "Preventing Refactoring Attacks on Software Plagiarism Detection through Graph-Based Structural Normalization"
<p>This repository stores all data used in the evaluation of the master's thesis "Preventing Refactoring Attacks on Software Plagiarism Detection through Graph-Based Structural Normalization". It ensures the continuous reproducibility of the results of the thesis.</p> <p>Content:</p> <ul> <li>JPlag v5.1.0 including the Java CPG frontend <ul> <li>Code base</li> <li>Runnable JAR</li> </ul> </li> <li>Data sets used for evaluation</li> <li>Evaluation results</li> <li>R script used to process the results</li> <li>Graphics and tables generated from the results</li> </ul> <p>The data sets were generated by Nils Niehues and Moritz Brödel and were originally published here:</p> <ul> <li><a href="../records/10430322">Supplementary Material for "Detecting Automatic Software Plagiarism via Token Sequence Normalization" (zenodo.org)</a></li> <li><a href="../records/10149536">Reproduction package for: Intelligent Match Merging to Prevent Obfuscation Attacks on Software Plagiarism Detectors (zenodo.org)</a></li> </ul> <p>The data sets are based partly on PROGPedia, available here:</p> <ul> <li><a href="../records/7449056">PROGpedia (zenodo.org)</a></li> </ul> <p>Visit <a title="State-of-the-Art Software Plagiarism & Collusion Detection" href="jplag.github.io/JPlag/" target="_blank" rel="noopener">JPlag</a> on GitHub for the current version.<br>See the thesis document for more information.</p> <p>Read about similar publications about Plagiarism Detection <a title="JPlag" href="https://jplag.github.io/MinimalLandingPage/" target="_blank" rel="noopener">here</a>.</p>
Fast Attack Graph Defense Localization via Bisimulation
<p>The submission will include the following artifacts:</p> <ul> <li>Paper: Fast Attack Graph Defense Localization via Bisimulation</li> <li>Datasets I-IV: hodlings security findings which used to generate attack graphs</li> <li>Evaluation script: used to create the tables and figure in the evaluation section from Results.csv</li> <li>Results and Figures: data analysis and visualization presented in the evaluation section<br><br></li> </ul>
Topologies, Checkpoints, and Configurations for the paper "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models"
<p>This repository consists of the <strong>files</strong> related to the <strong>paper</strong> "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models". In particular, this repository contains tensorboard logs, topologies, checkpoints, seeds, and results to ensure reproducibility of the results of the paper.</p> <p>The results included are related to the training/validation and hyper-parameters optimization of the outcome multi-label classifier, the GVI-RL agent, and the world model.<br>The data folder contains also the topologies used in the study, the vulnerabilities data used to generate them and the dataset for multi-label classification.</p> <p>The README.md file describes the folders' structure.</p>
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International Brain Laboratory public data
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OpenNeuro
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