A Security Vulnerability Threat Classification Method

Hou, Yuanwei; Ren, Xingzhang; Hao, Yongle; Mo, Tong; Li, Weiping

doi:10.1007/978-3-319-69811-3_38

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is important to patch the critical-first vulnerabilities [46]. Thus, besides CVSS, there have been many effective evaluation schemes for SVs [47], [48], [49].…”

Section: A Software Vulnerability Analyticsmentioning

confidence: 99%

Automated Software Vulnerability Assessment with Concept Drift

Sabir

Babar

2019

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

View full text Add to dashboard Cite

Software Engineering researchers are increasingly using Natural Language Processing (NLP) techniques to automate Software Vulnerabilities (SVs) assessment using the descriptions in public repositories. However, the existing NLP-based approaches suffer from concept drift. This problem is caused by a lack of proper treatment of new (out-of-vocabulary) terms for the evaluation of unseen SVs over time. To perform automated SVs assessment with concept drift using SVs' descriptions, we propose a systematic approach that combines both character and word features. The proposed approach is used to predict seven Vulnerability Characteristics (VCs). The optimal model of each VC is selected using our customized time-based crossvalidation method from a list of eight NLP representations and six well-known Machine Learning models. We have used the proposed approach to conduct large-scale experiments on more than 100,000 SVs in the National Vulnerability Database (NVD). The results show that our approach can effectively tackle the concept drift issue of the SVs' descriptions reported from 2000 to 2018 in NVD even without retraining the model. In addition, our approach performs competitively compared to the existing word-only method. We also investigate how to build compact concept-drift-aware models with much fewer features and give some recommendations on the choice of classifiers and NLP representations for SVs assessment.

show abstract

“…It is important to patch the critical-first vulnerabilities [46]. Thus, besides CVSS, there have been many effective evaluation schemes for SVs [47], [48], [49].…”

Section: A Software Vulnerability Analyticsmentioning

confidence: 99%

Automated Software Vulnerability Assessment with Concept Drift

Sabir

Babar

2019

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

View full text Add to dashboard Cite

show abstract

A Security Vulnerability Threat Classification Method

Cited by 1 publication

References 7 publications

Automated Software Vulnerability Assessment with Concept Drift

Automated Software Vulnerability Assessment with Concept Drift

Contact Info

Product

Resources

About