Automatically assessing vulnerabilities discovered by compositional analysis-Reference-Cited by-同舟云学术

Automatically assessing vulnerabilities discovered by compositional analysis

Published:2018-09-03 Issue: Volume: Page:
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Container-title:Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis
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Author:

Ognawala Saahil¹,Amato Ricardo Nales¹,Pretschner Alexander¹,Kulkarni Pooja¹

Affiliation:

1. TU Munich, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3243127.3243130

Reference37 articles.

1. {n. d.}. GNOME Bugzilla. https://bugzilla.gnome.org/. {n. d.}. GNOME Bugzilla. https://bugzilla.gnome.org/.

2. {n. d.}. National Vulnerability Database (NVD). https://nvd.nist.gov/. 2018. CVSS3 assessment using machine learning. https://osf.io/g87ny/?view_ only=21bf55c48263432f9b00702f84b23711 {n. d.}. National Vulnerability Database (NVD). https://nvd.nist.gov/. 2018. CVSS3 assessment using machine learning. https://osf.io/g87ny/?view_ only=21bf55c48263432f9b00702f84b23711

3. L. Allodi and F. Massacci. 2014. Comparing vulnerability severity and exploits using case-control studies. ACM Transactions on Information and System Security (TISSEC) (2014). 10.1145/2630069 L. Allodi and F. Massacci. 2014. Comparing vulnerability severity and exploits using case-control studies. ACM Transactions on Information and System Security (TISSEC) (2014). 10.1145/2630069

4. Threats on building models from CVS and Bugzilla repositories

5. What makes a good bug report?

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