{"type":"ScholarlyArticle","title":"VEST: A System for Vulnerability Exploit Scoring & Timing","authors":[{"name":"Haipeng Chen","slug":"haipeng-chen","given":"Haipeng","family":"Chen","affiliation":"Dartmouth College ,"},{"name":"Jing Liu","slug":"jing-liu","given":"Jing","family":"Liu","affiliation":"George Mason University"},{"name":"Rui Liu","slug":"rui-liu","given":"Rui","family":"Liu","affiliation":"Dartmouth College ,"},{"name":"Noseong Park","slug":"noseong-park","given":"Noseong","family":"Park","affiliation":"George Mason University"},{"name":"V. S. Subrahmanian","slug":"v-s-subrahmanian","given":"V. S.","family":"Subrahmanian","affiliation":"Dartmouth College ,"}],"abstract":"Knowing if/when a cyber-vulnerability will be exploited and how severe the vulnerability is can help enterprise security officers (ESOs) come up with appropriate patching schedules. Today, this ability is severely compromised: our study of data from Mitre and NIST shows that on average there is a 132 day gap between the announcement of a vulnerability by Mitre and the time NIST provides an analysis with severity score estimates and 8 important severity attributes. Many attacks happen during this very 132-day window. We present Vulnerability Exploit Scoring \\& Timing (VEST), a system for (early) prediction and visualization of if/when a vulnerability will be exploited, and its estimated severity attributes and score.","doi":"10.24963/ijcai.2019/937","url":"https://ijcai.org/proceedings/2019/937","pdf":"https://ijcai.org/proceedings/2019/0937.pdf","markdown":"https://ijcai.org/proceedings/2019/937.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/937","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Demos","track":"Demos","number":937,"pages":{"first":6503,"last":6505},"keywords":["Applications: Security and surveillance","Applications: Defense","AI: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Bullough et al., 2017] Benjamin L Bullough, Anna K Yanchenko, Christopher L Smith, and Joseph R Zipkin. 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