An automatic method for CVSS score prediction using vulnerabilities description-Reference-Cited by-同舟云学术

An automatic method for CVSS score prediction using vulnerabilities description

Published:2015-08-25 Issue:1 Volume:30 Page:89-96
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Khazaei Atefeh,Ghasemzadeh Mohammad,Derhami Vali

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference9 articles.

1. Cai D. , Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning, PhD Thesis, Department of Computer Science, UIUC 2009.

2. Han J. , Kamber M. and Pei J. , Data Mining: Concepts and Techniques. Third Edition, Morgan Kaufmann Publishers, 2011.

3. Jang J.S. , Sun C.T. and Mizutani E. , Neuro-Fuzzy and Soft Computing, Prentice Hall, 1997.

4. Wang L.X. , A Course in Fuzzy Systems and Control, Prentice Hall International Inc, 1997.

5. Bozorgi M. , Saul L.K. , Savage S. and Voelker G.M. , Beyond Heuristics: Learning to Classify Vulnerabilities and Predict Exploits. KDD’10, 2010.

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