prosved
Differences
This shows you the differences between two versions of the page.
Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
prosved [2024/05/18 22:01] – [International conferences] carlosesteban.budde@unitn.it | prosved [2024/11/25 21:16] (current) – [Dissemination & events] carlosesteban.budde@unitn.it | ||
---|---|---|---|
Line 17: | Line 17: | ||
* URL: https:// | * URL: https:// | ||
+ | This website reflects only the author' | ||
===== Objective and approach ===== | ===== Objective and approach ===== | ||
Line 50: | Line 51: | ||
==== 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' | While time-dependence of exploits and vulnerabilities is agreed upon by the practitioners' | ||
Line 65: | Line 66: | ||
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.// | ||
{{: | {{: | ||
- | |||
- | 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. | ||
Line 74: | Line 75: | ||
Depending on the severity of the vulnerability, | Depending on the severity of the vulnerability, | ||
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 " | ||
===== Real-world examples and applications ===== | ===== Real-world examples and applications ===== | ||
Line 125: | Line 128: | ||
- __Year__: 2023 | - __Year__: 2023 | ||
- **// | - **// | ||
- | - __Authors__: | + | - __Authors__: |
- __Journal__: | - __Journal__: | ||
- __DOI__: [[https:// | - __DOI__: [[https:// | ||
Line 135: | Line 138: | ||
- __Year__: 2022 | - __Year__: 2022 | ||
==== International conferences ==== | ==== International conferences ==== | ||
- | - :!: FIG cybersec | + | - **//Digging for Decision Trees: A Case Study in Strategy Sampling and Learning// |
+ | - __Authors__: Carlos E. Budde, Pedro R. D' | ||
+ | - __Conference__: | ||
+ | - __DOI__: (to appear) | ||
+ | - __Year__: 2024 | ||
+ | - **//Tools at the Frontiers of Quantitative Verification// | ||
+ | - __Authors__: | ||
+ | - __Conference__: | ||
+ | - __DOI__: [[https:// | ||
+ | - __Year__: 2024 | ||
+ | - **// | ||
+ | - __Authors__: | ||
+ | - __Conference__: | ||
+ | - __DOI__: [[https:// | ||
+ | - __Year__: 2024 | ||
====== Dissemination & events ====== | ====== 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: | 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: | ||
+ | * **Speck& | ||
+ | * Presentation video: https:// | ||
+ | * Presentation slides: https:// | ||
+ | * //Trento, IT// | ||
+ | * **ProSVED meeting**: [[https:// | ||
+ | * Presentation slides: {{ :: | ||
+ | * //Trento, IT// | ||
+ | * **SMARTITUDE GM' | ||
+ | * Presentation slides: {{ :: | ||
+ | * //Canazei, IT// | ||
+ | * **PI stories**: [[https:// | ||
+ | * Presentation slides: {{ :: | ||
+ | * //Trento, IT// | ||
+ | * **Lorentz Workshop**: [[https:// | ||
+ | * Presentation slides: {{ :: | ||
+ | * //Leiden, NL// | ||
* **SFSCON**: [[https:// | * **SFSCON**: [[https:// | ||
* Presentation video: https:// | * Presentation video: https:// | ||
* Presentation slides: https:// | * Presentation slides: https:// | ||
* //Bolzano, IT// | * //Bolzano, IT// | ||
- | * **Lorentz Workshop**: [[https:// | ||
- | * Presentation slides: {{ :: | ||
- | * //Leiden, NL// | ||
- | * **SMARTITUDE**: | ||
- | * Presentation slides: {{ :: | ||
- | * //Salerno, IT// | ||
* **Vuln4Cast**: | * **Vuln4Cast**: | ||
* Presentation slides: | * Presentation slides: | ||
* //Cardiff, UK// | * //Cardiff, UK// | ||
+ | * **SMARTITUDE kickoff**: formal models for security vulnerabilities in Smart Contracts | ||
+ | * Presentation slides: {{ :: | ||
+ | * //Salerno, IT// | ||
+ | * **Privacy Symposium**: | ||
+ | * Presentation slides: {{ :: | ||
+ | * //Venice, IT// | ||
+ | |||
+ | {{ Speck_and_Tech_meetup.jpg? | ||
====== Special thanks ====== | ====== Special thanks ====== | ||
Line 170: | Line 204: | ||
* 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.1716062462.txt.gz · Last modified: by carlosesteban.budde@unitn.it