Analysis of Software Vulnerabilities Using Machine Learning Techniques

被引：0

作者：

Diako, Doffou Jerome ^{[1
]}

Achiepo, Odilon Yapo M. ^{[2
]}

Mensah, Edoete Patrice ^{[3
]}

机构：

[1] INPHB Yamoussoukro, EDP, Yamoussoukro, Cote Ivoire

[2] Peleforo Gon Coulibaly Univ, Korhogo, Cote Ivoire

[3] INPHB Yamoussoukro, Yamoussoukro, Cote Ivoire

来源：

E-INFRASTRUCTURE AND E-SERVICES FOR DEVELOPING COUNTRIES (AFRICOMM 2019) | 2020年 / 311卷

关键词：

Machine learning; Vulnerabilities; Naive Bayes; Support vectors machines; CVSS;

D O I：

10.1007/978-3-030-41593-8_3

中图分类号：

F0 [经济学]; F1 [世界各国经济概况、经济史、经济地理]; C [社会科学总论];

学科分类号：

0201 ; 020105 ; 03 ; 0303 ;

摘要：

With the increasing development of software technologies, we see that software vulnerabilities are a very critical issue of IT security. Because of their serious impacts, many different approaches have been proposed in recent decades to mitigate the damage caused by software vulnerabilities. Machine learning is also part of an approach to solve this problem. The main objective of this document is to provide three supervised machine to predict software vulnerabilities from a dataset of 6670 observations from national vulnerabilities database (NVD). The effectiveness of the proposed models has been evaluated with several performance indicators including Accuracy.

引用

页码：30 / 37

页数：8

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