Machine Learning Based Prediction of Vulnerability Information Subject to a Security Alert

Ryu Watanabe, Takashi Matsunaka, Ayumu Kubota, Jumpei Urakawa

2023

Abstract

The security alerts announced by various organizations can be used as an indicator of the severity and danger of vulnerabilities. The alerts are public notifications issued by security-related organizations or product/software vendors. The experts from such organizations determine whether it is a necessity of a security alert based on the published vulnerability information, threats, and publicized damages caused by the attacks to warn the public of high-risk vulnerabilities or cyberattacks. However, it may take some time between the disclosure of the vulnerability and the release of a security alert. If this delay can be shortened, it will be possible to guess the severity of the vulnerability earlier. For this purpose, the authors have proposed a machine learning method to predict whether a disclosed vulnerability is severe enough to publicize a security alert. In this paper, our proposed scheme and the evaluation we conduct to verify its accuracy are denoted.

Paper Citation

in Harvard Style

Watanabe R., Matsunaka T., Kubota A. and Urakawa J. (2023). Machine Learning Based Prediction of Vulnerability Information Subject to a Security Alert. In Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-624-8, pages 313-320. DOI: 10.5220/0011613700003405

in Bibtex Style

@conference{icissp23,
author={Ryu Watanabe and Takashi Matsunaka and Ayumu Kubota and Jumpei Urakawa},
title={Machine Learning Based Prediction of Vulnerability Information Subject to a Security Alert},
booktitle={Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2023},
pages={313-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011613700003405},
isbn={978-989-758-624-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Machine Learning Based Prediction of Vulnerability Information Subject to a Security Alert
SN - 978-989-758-624-8
AU - Watanabe R.
AU - Matsunaka T.
AU - Kubota A.
AU - Urakawa J.
PY - 2023
SP - 313
EP - 320
DO - 10.5220/0011613700003405