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23 results for “Vulnerability Detection”
A collection of datasets for software vulnerability detection
<p>This is a collection of datasets that are used for AI-based software vulnerability detection. All the datasets are in the .csv format and each row represents a sample. Each dataset includes a set of functions written in C and the target of each function is either 0 (non-vulnerable) or 1 (vulnerable).</p> <ol> <li><strong>data_C_Lin2017_test.csv:</strong> <ul> <li>Reference paper: <a href="https://dl.acm.org/doi/10.1145/3133956.3138840">Vulnerability Discovery with Function Representation Learning from Unlabeled Projects</a>, 2017.</li> <li>Data source on GitHub: <a href="https://github.com/DanielLin1986/function_representation_learning">https://github.com/DanielLin1986/function_representation_learning</a></li> <li>This dataset includes 44 vulnerable and 577 non-vulnerable functions from the LibPNG project.</li> </ul> </li> <li><strong>data_C_LineVul_test.csv:</strong> <ul> <li>Reference paper: <a href="https://ieeexplore.ieee.org/document/9796256">LineVul: A Transformer-based Line-Level Vulnerability Prediction</a>, 2022.</li> <li>Data source on Hugging Face: <a href="https://huggingface.co/datasets/Partha117/LineVul_Test_Dataset">https://huggingface.co/datasets/Partha117/LineVul_Test_Dataset</a></li> <li>This dataset includes 1055 vulnerable and 17809 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_PrimeVul_test.csv:</strong> <ul> <li>Reference paper: <a href="https://arxiv.org/abs/2403.18624">Vulnerability Detection with Code Language</a><br><a href="https://arxiv.org/abs/2403.18624">Models: How Far Are We?</a> 2024.</li> <li>Data source on GitHub: <a href="https://github.com/DLVulDet/PrimeVul">https://github.com/DLVulDet/PrimeVul</a></li> <li>From the data source, the primevul_test.jsonl was used to created this dataset.</li> <li>This dataset includes 695 vulnerable and 25213 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_Choi2017_test.csv:</strong> <ul> <li>Reference paper: <a href="https://www.ijcai.org/proceedings/2017/0214.pdf">End-to-End Prediction of Buffer Overruns from Raw Source Code</a><br><a href="https://www.ijcai.org/proceedings/2017/0214.pdf">via Neural Memory Networks</a>, 2017.</li> <li>Data source on GitHub: <a href="https://github.com/mjc92/buffer_overrun_memory_networks">https://github.com/mjc92/buffer_overrun_memory_networks</a></li> <li>From GitHub, all the data in trainnig_100.txt, test_1_100.txt, test_2_100.txt,test_3_100.txt,test_4_100.txt, and corresponding _labels.txt files are combined to create this dataset.</li> <li>This dataset includes 7054 vulnerable and 6946 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_Devign_test.csv:</strong> <ul> <li>Reference paper: <a href="https://proceedings.neurips.cc/paper_files/paper/2019/file/49265d2447bc3bbfe9e76306ce40a31f-Paper.pdf">Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks</a>, 2019</li> <li>Data source on Hugging Face: <a href="https://huggingface.co/datasets/claudios/code_x_glue_devign">https://huggingface.co/datasets/claudios/code_x_glue_devign</a></li> <li>From Hugging Face, all the data in train, validation, and test are combined to create this dataset.</li> <li>This dataset includes 12460 vulnerable and 14858 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_Ours_{train,test}.csv:</strong> <ul> <li>This dataset is manually collected from projects on GitHub that have registered CVEs into NVD from 2002 to 2023. The 6,766 non-vulnerable code functions are extracted from the <a href="https://dl.acm.org/doi/10.1145/3607199.3607242">DiverseVul dataset</a> to increase the code diversity. </li> <li>This training set includes 5413 vulnerable and 5413 non-vulnerable functions.</li> <li>The test set includes 1353 vulnerable and 1353 non-vulnerable functions.</li> </ul> </li> </ol>
Software vulnerability detection datasets - function/method level
<p>This dataset is for software vulnerability detection and includes source code in eight programming languages (C, C++, Java, JavaScript, Go, PHP, Ruby, Python). All data is collected from GitHub.</p><p>data<i>{programming language}_vul.json: a set of vulnerable code samples in a certain programming language.</i></p><p>data<i>{programming language}_patch.json: a set of patching code samples in a certain programming language.</i></p><p> </p><p>Each source code sample includes the following 16 properties: </p><p><strong>index</strong>: index of code. If is_vulnerable==False, this index indicates that this code is a patch of the indexing vulnerable code.</p><p><strong>code</strong>: raw source code (may include comments).</p><p><strong>is_vulnerable</strong>: the code is vulnerable (<strong>True</strong>) or a patch (<strong>False</strong>).</p><p><strong>programming_language</strong>: programming language of the code.</p><p><strong>method_name</strong>: name of the method.</p><p><strong>file_name</strong>: name of the file where the source code is extracted.</p><p><strong>repo_url</strong>: url of the project repository.</p><p><strong>repo_owner</strong>: owner of the repository.</p><p><strong>committer</strong>: developer who pushed the commit.</p><p><strong>committer_date</strong>: date when the commit was pushed.</p><p><strong>commit_msg</strong>: the commit message.</p><p><strong>cwe_id</strong>: If is_vulnerable==True, the CWE id; otherwise None.</p><p><strong>cwe_name</strong>: If is_vulnerable==True, the name of corresponding CWE; otherwise None.</p><p><strong>cwe_description</strong>: If is_vulnerable==True, the description of corresponding CWE; otherwise None.</p><p><strong>cwe_url</strong>: If is_vulnerable==True, the url to obtain more details of corresponding CWE; otherwise None.</p><p><strong>cve_id</strong>: If is_vulnerable==True, the CVE id; otherwise None.</p>
Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches (Replication Package Part 3: OpenSSL dataset)
<h1><strong>The Replication Package of</strong></h1> <h1><strong>"Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches"</strong></h1> <h3><strong>Part 3 (OPENSSL Dataset)</strong></h3> <div> <div>This repository includes:</div> <ol> <li><em><strong>Code.zip</strong></em> that contains the codes to replicate some parts of this study:<br>a. <em>1_generate_datasets</em> implements our methodology to generate the datasets.<br>b. <em>2_run_models</em> runs the ML models during the evaluation.<br>c. <em>3_result_replication </em>generates charts presented in the paper from the ML evaluation results.</li> <li><em><strong>Datasets.zip</strong></em> that contain 2 folders:<br>a. <em>original</em> datasets: 1 from <a href="https://github.com/CGCL-codes/VulDeePecker" target="_blank" rel="noopener">NVD Vuldeepecker</a> and 3 extracted from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a>.<br> <div> <div>b. <em>OPENSSL</em> datasets: train, validation, test sets for each time of observation extracted using our methodology from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a> dataset for project <em>openssl</em>.</div> </div> </li> <li><em><strong>Pretrained-models.zip</strong></em> that we generated during our evaluation (3 test results for each time point in the timeline [2013-2019]).</li> <li><em><strong>Results.zip</strong></em> of our evaluation, the folder <em>ALL</em> contains the overall results and other folders are results by model.</li> </ol> <p><strong>UPDATED version 5<br></strong>- added a GLOBAL_README.md which contains the 3 stages and how they are connected to each other<br>- updated LineVul.ipynb: import AdamW from torch.optim instead of transformers<br>- updated README.md in Code2Vec with the prerequisites of Java to run gradlew for astmine</p> <p><strong>UPDATED version 6<br></strong>- updated CodeBert.ipynb: import AdamW from torch.optim instead of transformers</p> <p>Documentations</p> <ol> <li><em><strong>INSTALL.pdf </strong></em>: how to install the codes</li> <li><em><strong>README.pdf</strong></em>: readme file</li> <li><em><strong>REQUIREMENTS.pdf</strong></em>: hardware and software requirements</li> <li><em><strong>STATUS.pdf</strong></em> : status for artifact submission</li> <li><em><strong>LICENSE.pdf</strong></em>: the license of this artifact</li> <li><em><strong>PAPER.pdf</strong></em>: the camera-ready version of the paper</li> </ol> </div> <div> <div>Please refer to the following repositories for the other datasets and pre-trained models:</div> <div>- Part 1 NVD Vuldeeepecker : <a href="https://doi.org/10.5281/zenodo.8207883" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8207883</a></div> - Part 2 LINUX : <a href="https://doi.org/10.5281/zenodo.10960662" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10960662</a><br> <div>- Part 4 POPPLER : <a href="https://doi.org/10.5281/zenodo.14713143">https://doi.org/10.5281/zenodo.14713143</a></div> <div> </div> <div>This work was partly funded by the EU under the H2020 Program AssureMOSS (Grant n. 952647) and the Horizon Europe Program Sec4AI4Sec (Grant n. 101120393), by the Italian Ministry of University and Research (MUR) under the P.N.R.R. – NextGenerationEU grant n.\ PE00000014 (SERICS subproject COVERT), and by the Dutch Research Council (NWO) under the grant NWA.1215.18.006 (Theseus) and grant KIC1.VE01.20.004 (HEWSTI). </div> </div>
Data and Material for 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'
<p>Data and Material supporting the paper 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'.</p>
Artifacts for the ISSTA 2022 Paper: An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection
<p>This repository contains the evaluation script and the corresponding data of the ISSTA'22 paper "An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection".</p>
An End-to-End Framework for Detecting and Repairing Potential Vulnerabilities
<p>Nowadays, program development is getting easier and easier as the various IDE tools provide advice on what to write in the program. But it is not enough to implement a solution to a problem; it is also important that the non-functional properties, like the quality or security of the code, are appropriate in all aspects. One of the most widely used techniques to ensure quality is testing. If the tests fail, one can fix the code immediately. However, security issues are unexpected cases when implementing the program, which is why we do not write tests for them in advance. In many cases, security-relevant bugs can not only cause financial loss but also put human lives at risk, so detecting and fixing them is an important step for the reliability and quality of the program. The tool presented in this paper aims to generate automatic code repairs to potential vulnerabilities in the program. By integrating the recommended fixes, one can easily harden the security of their program early in the development process. A case study on six open-source Java subject systems showed that we were able to generate viable repair patches for 57 out of the 81 detected security issues (70%). For certain types (e.g., revealing private references of mutable objects), our tool reached close to perfect performance.</p>
Detection of Areas with Human Vulnerability Using Public Satellite Images and Deep Learning (Dataset)
<div> <h2>Overview</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL#overview"></a></div> <p>This repository contains the code and resources for the project titled <strong>"Detection of Areas with Human Vulnerability Using Public Satellite Images and Deep Learning"</strong>. The goal of this project is to identify regions where individuals are living under precarious conditions and facing neglected basic needs, a situation often seen in Brazil. This concept is referred to as "human vulnerability" and is exemplified by families living in inadequate shelters or on the streets in both urban and rural areas.</p> <p>Focusing on the Federal District of Brazil as the research area, this project aims to develop two novel public datasets consisting of satellite images. The datasets contain imagery captured at 50m and 100m scales, covering regions of human vulnerability, traditional areas, and improperly disposed waste sites.</p> <p>The project also leverages these datasets for training deep learning models, including <strong>YOLOv7</strong> and other state-of-the-art models, to perform image segmentation. A comparative analysis is conducted between the models using two training strategies: training from scratch with random weight initialization and fine-tuning using pre-trained weights through <strong>transfer learning</strong>.</p> <div> <h3>Key Achievements</h3> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL#key-achievements"></a></div> <ul> <li>Two new satellite image datasets focusing on human vulnerability and improperly disposed waste sites, available in public domains.</li> <li>Comparison of image segmentation models, including <strong>YOLOv7</strong> and <strong>Segmentation Models</strong>, with performance metrics.</li> <li>Best F1-scores: 0.55 for <strong>YOLOv7</strong> and 0.64 for <strong>Segmentation Models</strong>.</li> </ul> <p>This repository provides the code, models, and data pipelines used for training, evaluation, and performance comparison of these deep learning models.<br><br></p> <div> <h2>Citation (Bibtex)</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL?tab=readme-ov-file#citation-bibtex"></a></div> <pre><code>@TECHREPORT {TechReport-Julia-Laura-HumanVulnerability-2024, author = "Julia Passos Pontes, Laura Maciel Neves Franco, Flavio De Barros Vidal", title = "Detecção de Áreas com Atividades de Vulnerabilidade Humana utilizando Imagens Públicas de Satélites e Aprendizagem Profunda", institution = "University of Brasilia", year = "2024", type = "Undergraduate Thesis", address = "Computer Science Department - University of Brasilia - Asa Norte - Brasilia - DF, Brazil", month = "aug", note = "People living in precarious conditions and with their basic needs neglected is an unfortunate reality in Brazil. This scenario will be approached in this work according to the concept of \"human vulnerability\" and can be exemplified through families who live in inadequate shelters, without basic structures and on the streets of urban or rural centers. Therefore, assuming the Federal District as the research scope, this project proposes to develop two new databases to be made available publicly, considering the map scales of 50m and 100m, and composed by satellite images of human vulnerability areas, regions treated as traditional and waste disposed inadequately. Furthermore, using these image bases, trainings were done with the YOLOv7 model and other deep learning models for image segmentation. By adopting an exploratory approach, this work compares the results of different image segmentation models and training strategies, using random weight initialization (from scratch) and pre-trained weights (transfer learning). Thus, the present work was able to reach maximum F1 score values of 0.55 for YOLOv7 and 0.64 for other segmentation models." } </code></pre> <div> </div> <div> <h2>License</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL?tab=readme-ov-file#license"></a></div> <p>This project is licensed under the MIT License - see the LICENSE file for details.</p>
Data from: Detecting the effects of rapid tectonically-induced subsidence on Mayotte Island since 2018 on beach and reef morphology, and implications for coastal vulnerability to marine flooding
<p>This dataset contains data from the monitoring morphological evolution of beaches and coral reefs in Mayotte island. Mayotte, part of the coral reef-fringed Comoro archipelago in the SW Indian Ocean, experienced in 2018 and 2019 an intense seismic crisis. The repeated earthquake activity since May 2018 has been associated with deformation of the surface of Mayotte, resulting in land subsidence.</p> <p>The earlier 2006-2008 profiles were realized using a Leica TC 407® total station, and referenced to local IGN 50 benchmarks. The more recent 2019, 2020, and 2021 surveys were carried out using a GNSS differential Trimble R8S® system. Given the rapid subsidence that has affected Mayotte, the benchmarks used in this study, like others in Mayotte, need to be recalibrated by the IGN (French Institut Géographique National) and SHOM. This has still not yet been done, as the final outcome of the vertical island movements is still not clear.</p>
Thesis data: Enhancing Vulnerability Detection: A Comparative Study of Change Identification Methods Across Granularity Levels
<p>Starting dataset used within the thesis; Enhancing Vulnerability Detection: A Comparative Study of Change Identification Methods Across Granularity Levels.</p> <p>Results of manual annotation of and extract of nonPatchTaggedCommitLinks within the NVD CVE dataset.</p>
A Line-level Explainable Vulnerability Detection Approach for Java
<p>Given our modern society’s level of dependency on IT technology, high quality and security are not just desirable but rather vital properties of current software systems. Empirical methods leveraging the available rich open-source data and advanced data processing techniques of ML algorithms can help software developers ensure these properties. Nonetheless, state-of-the-art bug and vulnerability prediction methods are rarely used in practice due to numerous reasons. The predictions are not actionable in most of the cases due to their level of granularity (i.e., they mark entire classes/files to be buggy or vulnerable) and because the methods seldom provide explanation why a fragment of source code is problematic. In this paper, we present a novel Java vulnerability detection method that addresses both of these issues. It is an adaptation of our previous method for JavaScript that is capable of pinpointing vulnerable source code lines of a program together with a prototype-based explanation. The method relies on the word2vec similarity of code fragments to known vulnerable source code lines. Our empirical evaluation showed promising results, we could detect 61% and 41% of the vulnerable code lines by flagging only 43% and 22% of the program code lines, respectively, using two of the best detection configurations.</p> <p>The dataset contains the extracted vulnerable code lines and word2vec models used in the experiment on 205 Java projects.</p>
VULDEFF: Vulnerability detection method based on function fingerprints and code differences
<p>The dataset of VULDEFF. For more information, visit our <a href="https://github.com/das-lab/Vuldeff" target="_blank" rel="noopener">GitHub</a>.</p> <p>Due to the lack of large-scale, real, and uniformly formatted vulnerability datasets in the field of software security, we realize the automatic collection and processing of vulnerability patches and vulnerability source code and build a reliable and continuously updated patch and vulnerability source code dataset. The dataset includes 9,546 patch files of CVE and 24,920 unpatched vulnerable source code files, covering 464 programs, and can be extended to support other open source projects.</p>
A Systematic Evaluation of Automated Tools for Side-Channel Vulnerabilities Detection in Cryptographic Libraries
<p>Artifact for CCS'23 submission</p>
Vulnerability Detection for Smart Contracts: A Systematic Literature Review - Supplementary Material
<p>Supplementary Material refers to the paper "Vulnerability Detection for Smart Contracts: A Systematic Literature Review" and contains the following information:</p> <p>a) DataAnalysis.xls - An Excel file created for analysis (i.e., tables and graphics).</p> <p> </p>
Detection of Coronary Vulnerable Plaque With Contrast-enhanced Magnetic Resonance Imaging
ClinicalTrials.gov study NCT00984776. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Deep Learning-Based Methodology for Vulnerability Detection in Smart Contracts
<p>智能合约漏洞检测公共数据集</p>
Evaluating Deep Learning-based Vulnerability Detection Models on Realistic Datasets
<p>This package contains all the data we used for our experiments.<br> </p>
Graph D2A: D2A Dataset for Vulnerability Detection Transformed into ECPG Format for Training GNNs
<p>The Graph D2A is based on the dataset 'D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis'. Graph D2A was created by transforming individual samples from D2A into the form of Extended Code Property Graphs (ECPGs). The resulting dataset is designed for graph neural network models (GNNs) for detection on vulnerabilities in source code.</p> <p>Graph D2A was developed as part of the Master's thesis 'Evaluating Reliability of Static Analysis Results Using Machine Learning' at BUT FIT.</p>
Dataset for software vulnerability detection
<ol><li>raw_C.csv<ul><li>Raw (it may includes comments) source code in C.</li><li>Attributes:<ul><li>cwe_id</li><li>name: method name</li><li>repo_name: repository name</li><li>repo_url: repository url</li><li>old_path</li><li>file_change_id: used as a key in SQLite</li><li>method_change_id: used as a key in SQLite</li><li>code: source code</li><li>before_change: indicates whether the code is vulnerable (1) or not (0)</li></ul></li></ul></li><li>clean_C.csv<ul><li>Pre-processed (removed comments) source code in C.</li><li>Attributes (the same as raw_C.csv).</li></ul></li></ol>
Supplementary materials of the paper entitled: "Advanced Vulnerability Scanning for Open Source Software: Detection and Mitigation of Log4j Vulnerabilities"
<p>Evaluation dataset across 140 scans of 28 open-source repositories.</p>
Deep Learning-Based Methodology for Vulnerability Detection in Smart Contracts
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