Analysis of Software Vulnerabilities Using Machine Learning Techniques

  • Doffou Jerome DiakoEmail author
  • Odilon Yapo M. Achiepo
  • Edoete Patrice Mensah
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 311)


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.


Machine learning Vulnerabilities Naive Bayes Support vectors machines CVSS 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  • Doffou Jerome Diako
    • 1
    Email author
  • Odilon Yapo M. Achiepo
    • 2
  • Edoete Patrice Mensah
    • 3
  1. 1.EDPINPHB YamoussoukroYamoussoukroCôte d’Ivoire
  2. 2.Peleforo Gon Coulibaly UniversityKorhogoCôte d’Ivoire
  3. 3.INPHB YamoussoukroYamoussoukroCôte d’Ivoire

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