Empirical studies on the impact of filter‐based ranking feature selection on security vulnerability prediction

Empirical studies on the impact of filter‐based ranking feature selection on security vulnerability prediction

Abstract Security vulnerability prediction (SVP) can construct models to identify potentially vulnerable program modules via machine learning. Two kinds of features from different points of view are used to measure the extracted modules in previous studies. One kind considers traditional software me...

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Bibliographic Details
Main Authors: Xiang Chen, Zhidan Yuan, Zhanqi Cui, Dun Zhang, Xiaolin Ju
Format: Article
Language:English
Published: Wiley 2021-02-01
Series:IET Software
Subjects:
data mining
feature extraction
inspection
Internet
learning (artificial intelligence)
pattern classification
Online Access:https://doi.org/10.1049/sfw2.12006
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https://doi.org/10.1049/sfw2.12006

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