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prosved [2024/05/18 22:00] carlosesteban.budde@unitn.it [Journals] |
prosved [2024/07/30 22:36] (current) carlosesteban.budde@unitn.it [Special thanks] |
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| * URL: https://cordis.europa.eu/project/id/101067199 | * URL: https://cordis.europa.eu/project/id/101067199 | ||
| + | This website reflects only the author's view and is his sole responsibility. The European Commission's Research Executive Agency is not responsible for any use that may be made of the information it contains. | ||
| ===== Objective and approach ===== | ===== Objective and approach ===== | ||
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| ==== Quantitative forecasts of security vulnerabilities ==== | ==== Quantitative forecasts of security vulnerabilities ==== | ||
| - | TDTs (and ATs) offer optimal representations of codebases and their evolution in time, to allow quantitative studies of the propagation of security vulnerabilities---//but they do nothing to effectively quantify these probabilities.// | + | TDTs (and ATs) offer optimal representations of codebases and their evolution in time, to allow quantitative studies of the propagation of security vulnerabilities---but they do nothing to effectively quantify these probabilities. |
| For that, ProSVED poses the following broad research question: | For that, ProSVED poses the following broad research question: | ||
| - | > How does the probability of finding a security vulnerability in a software library evolve over time? | + | > //How does the probability of finding a security vulnerability in a software library evolve over time?// |
| While time-dependence of exploits and vulnerabilities is agreed upon by the practitioners' community---see e.g. [[https://www.first.org/cvss/v3.1/specification-document#Temporal-Metrics|the Temporal Metrics from the CVSS standard]]---the great majority of research has focused on the //detection// of vulnerabilities already known in the code. Some past attempts to generate vulnerability forecasts have used time-series machinery: one of the most modern and tangible outcomes is provided by the Vulnerability Forecasting interest group of FIRST, which is periodically updated to reflect yearly and quarterly projections of CVEs: https://github.com/FIRSTdotorg/Vuln4Cast/blob/main/README.md. | While time-dependence of exploits and vulnerabilities is agreed upon by the practitioners' community---see e.g. [[https://www.first.org/cvss/v3.1/specification-document#Temporal-Metrics|the Temporal Metrics from the CVSS standard]]---the great majority of research has focused on the //detection// of vulnerabilities already known in the code. Some past attempts to generate vulnerability forecasts have used time-series machinery: one of the most modern and tangible outcomes is provided by the Vulnerability Forecasting interest group of FIRST, which is periodically updated to reflect yearly and quarterly projections of CVEs: https://github.com/FIRSTdotorg/Vuln4Cast/blob/main/README.md. | ||
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| A hurdle is that, when considering an individual code base such as the source code of a single library, security vulnerabilities become rare events. This hinders statistical fitting and is commonly combated with data aggregation---cf. the Vulnerability Forecasting approach to work on the entire CVE dataset. | A hurdle is that, when considering an individual code base such as the source code of a single library, security vulnerabilities become rare events. This hinders statistical fitting and is commonly combated with data aggregation---cf. the Vulnerability Forecasting approach to work on the entire CVE dataset. | ||
| To generate more specific forecasts, ProSVED proposes divisions of the learning sets by attributes that are known or suspected to affect security vulnerability occurrence, such as library size, seniority of developers, and functional purpose. | To generate more specific forecasts, ProSVED proposes divisions of the learning sets by attributes that are known or suspected to affect security vulnerability occurrence, such as library size, seniority of developers, and functional purpose. | ||
| + | |||
| + | > //From a singled-out set of libraries, ProSVED measures the time elapsed between the release of the source code and the publication of a CVE for it, fitting statistical models to come up with probability density functions (PDFs) for the publication of a CVE since code release.// | ||
| {{:3dplot_joint_pdf_local_snl_lastver.png?300 |Probability of vulnerabilities in libraries with little exposure to the Internet}} | {{:3dplot_joint_pdf_local_snl_lastver.png?300 |Probability of vulnerabilities in libraries with little exposure to the Internet}} | ||
| - | |||
| - | From a singled-out set of libraries, ProSVED measures the time elapsed between the release of the source code and the publication of a CVE for it, fitting statistical models to come up with probability density functions (PDFs) for the publication of a CVE since code release. | ||
| This provides individual PDFs for specific types of codebases, that can be linked to the nodes that compose a TDT, by determining which type of library each such node represents. | This provides individual PDFs for specific types of codebases, that can be linked to the nodes that compose a TDT, by determining which type of library each such node represents. | ||
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| Depending on the severity of the vulnerability, or more fine-grained information such as the potential attack vector, this can represent a disruptive event that forces the release of urgent patches. | Depending on the severity of the vulnerability, or more fine-grained information such as the potential attack vector, this can represent a disruptive event that forces the release of urgent patches. | ||
| Quantifying these probabilities gives companies concrete estimates of the workload needed in the future, thus facilitating security-related decisions. | Quantifying these probabilities gives companies concrete estimates of the workload needed in the future, thus facilitating security-related decisions. | ||
| + | |||
| + | ProSVED has also studied analytical (or rather, numerical) compositions of the PDFs to spawn the multi-dimensional probabilistic space that describes the fluctuation of vuln. probability as a function of time in dense non-singular intervals. In layman terms, one can see the full landscape of "vulnerability probability" up to a chosen future moment in time. While this suffers from the curse of dimensionality, which renders it impractical to visualize all dependencies of a project, it allows to single out a few codebases---e.g. dependencies of main concern, usual suspects---and study them in greater detail than via TDT analysis, which can only produce punctual aggregated results. | ||
| ===== Real-world examples and applications ===== | ===== Real-world examples and applications ===== | ||
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| - __Year__: 2023 | - __Year__: 2023 | ||
| - **//Automated fault tree learning from continuous-valued sensor data: a case study on domestic heaters//** | - **//Automated fault tree learning from continuous-valued sensor data: a case study on domestic heaters//** | ||
| - | - __Authors__: Bart Verkuil1, Carlos E. Budde, Doina Bucur | + | - __Authors__: Bart Verkuil, Carlos E. Budde, Doina Bucur |
| - __Journal__: International Journal of Prognostics and Health Management | - __Journal__: International Journal of Prognostics and Health Management | ||
| - __DOI__: [[https://papers.phmsociety.org/index.php/ijphm/article/view/3160|https://doi.org/10.36001/ijphm.2022.v13i2.3160]] | - __DOI__: [[https://papers.phmsociety.org/index.php/ijphm/article/view/3160|https://doi.org/10.36001/ijphm.2022.v13i2.3160]] | ||
| Line 135: | Line 138: | ||
| - __Year__: 2022 | - __Year__: 2022 | ||
| ==== International conferences ==== | ==== International conferences ==== | ||
| + | - **//Transient Evaluation of Non-Markovian Models by Stochastic State Classes and Simulation//** | ||
| + | - __Authors__: Gabriel Dengler, Laura Carnevali, Carlos E. Budde, Enrico Vicario | ||
| + | - __Conference__: [[https://www.qest-formats.org/papers.html|QEST+FORMATS 2024]] | ||
| + | - __Paper__: in press---but check this prepring [[https://arxiv.org/abs/2406.16447|in arXiv]] | ||
| + | - __Year__: 2024 (to appear) | ||
| - :!: FIG cybersec | - :!: FIG cybersec | ||
| - | - :!: ??? | ||
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| A social objective of ProSVED is to raise awareness of cybersecurity practices in general, and the importance (and feasibility) of forecasting security vulnerabilities in particular. In this sense, ProSVED has been part of the following scientific and industrial dissemination events: | A social objective of ProSVED is to raise awareness of cybersecurity practices in general, and the importance (and feasibility) of forecasting security vulnerabilities in particular. In this sense, ProSVED has been part of the following scientific and industrial dissemination events: | ||
| + | * **ProSVED meeting**: [[https://webmagazine.unitn.it/en/evento/disi/121125/prosved-project-closing-event|Final event]] | ||
| + | * Presentation slides: {{ ::talk_prosved_final.pdf |}} | ||
| + | * //Trento, IT// | ||
| + | * **SMARTITUDE GM'24**: quantifying risk (impact) of Smart Contracts vulnerabilities | ||
| + | * Presentation slides: {{ ::talk_smartitude_2024.pdf |}} | ||
| + | * //Canazei, IT// | ||
| + | * **PI stories**: [[https://webmagazine.unitn.it/en/evento/drict/120901/third-times-the-charm|Third time's the charm]] | ||
| + | * Presentation slides: {{ ::talk_pi_seminar_2024.pdf |}} | ||
| + | * //Trento, IT// | ||
| + | * **Lorentz Workshop**: [[https://www.lorentzcenter.nl/predictive-maintenance-let-data-maintain-the-model.html|Predictive Maintenance: Let Data Maintain the Model]] | ||
| + | * Presentation slides: {{ ::talk_lorentz_2023.pdf |}} | ||
| + | * //Leiden, NL// | ||
| * **SFSCON**: [[https://www.sfscon.it/|South Tyrol Free Software Conference]] | * **SFSCON**: [[https://www.sfscon.it/|South Tyrol Free Software Conference]] | ||
| * Presentation video: https://vimeo.com/886816725 | * Presentation video: https://vimeo.com/886816725 | ||
| * Presentation slides: https://www.slideshare.net/slideshow/sfscon23-carlos-esteban-budde-predict-security-attacks-in-foss/264283292?from_search=0 | * Presentation slides: https://www.slideshare.net/slideshow/sfscon23-carlos-esteban-budde-predict-security-attacks-in-foss/264283292?from_search=0 | ||
| * //Bolzano, IT// | * //Bolzano, IT// | ||
| - | * **Lorentz Workshop**: [[https://www.lorentzcenter.nl/predictive-maintenance-let-data-maintain-the-model.html|Predictive Maintenance: Let Data Maintain the Model]] | ||
| - | * Presentation slides: {{ ::talk_lorentz_2023.pdf |}} | ||
| - | * //Leiden, NL// | ||
| - | * **SMARTITUDE**: formal models for security vulnerabilities in Smart Contracts | ||
| - | * Presentation slides: {{ ::talk_smartitude_2023.pdf |}} | ||
| - | * //Salerno, IT// | ||
| * **Vuln4Cast**: [[https://www.first.org/events/colloquia/cardiff2023/|FIRST group technical colloquium]] | * **Vuln4Cast**: [[https://www.first.org/events/colloquia/cardiff2023/|FIRST group technical colloquium]] | ||
| * Presentation slides: https://www.first.org/resources/papers/cardiff2023/Vuln4Cast-Budde.-Paramitha.-Massacci.pdf | * Presentation slides: https://www.first.org/resources/papers/cardiff2023/Vuln4Cast-Budde.-Paramitha.-Massacci.pdf | ||
| * //Cardiff, UK// | * //Cardiff, UK// | ||
| + | * **SMARTITUDE kickoff**: formal models for security vulnerabilities in Smart Contracts | ||
| + | * Presentation slides: {{ ::talk_smartitude_2023.pdf |}} | ||
| + | * //Salerno, IT// | ||
| + | * **Privacy Symposium**: [[https://sites.grenadine.co/sites/iot/en/2022-privacy-symposium-conference/schedule/8529/CyberSec4Europe%20-%20Research%20to%20Innovation%3A%20Common%20Research%20Framework%20on%20Security%20and%20Privacy|Research to Innovation: Common Research Framework on Security and Privacy]] | ||
| + | * Presentation slides: {{ ::talk_psymp_2022.pdf |}} | ||
| + | * //Venice, IT// | ||
| ====== Special thanks ====== | ====== Special thanks ====== | ||
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| * D. Di Nucci (Univ. of Salerno, IT) | * D. Di Nucci (Univ. of Salerno, IT) | ||
| * G. Di Tizio (Airbus, FR) | * G. Di Tizio (Airbus, FR) | ||
| + | * El Rulo y su Kepler Kompilator | ||
prosved.1716062451.txt.gz ยท Last modified: 2024/05/18 22:00 by carlosesteban.budde@unitn.it
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