Challenging machine learning algorithms in predicting vulnerable javascript functions

Rudolf Ferenc, Peter Hegedus, Peter Gyimesi, Gabor Antal, Denes Ban, Tibor Gyimothy

Research output: Conference contribution

1 Citation (Scopus)

Abstract

The rapid rise of cyber-crime activities and the growing number of devices threatened by them place software security issues in the spotlight. As around 90% of all attacks exploit known types of security issues, finding vulnerable components and applying existing mitigation techniques is a viable practical approach for fighting against cyber-crime. In this paper, we investigate how the state-of-the-art machine learning techniques, including a popular deep learning algorithm, perform in predicting functions with possible security vulnerabilities in JavaScript programs. We applied 8 machine learning algorithms to build prediction models using a new dataset constructed for this research from the vulnerability information in public databases of the Node Security Project and the Snyk platform, and code fixing patches from GitHub. We used static source code metrics as predictors and an extensive grid-search algorithm to find the best performing models. We also examined the effect of various re-sampling strategies to handle the imbalanced nature of the dataset. The best performing algorithm was KNN, which created a model for the prediction of vulnerable functions with an F-measure of 0.76 (0.91 precision and 0.66 recall). Moreover, deep learning, tree and forest based classifiers, and SVM were competitive with F-measures over 0.70. Although the F-measures did not vary significantly with the re-sampling strategies, the distribution of precision and recall did change. No re-sampling seemed to produce models preferring high precision, while re-sampling strategies balanced the IR measures.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8-14
Number of pages7
ISBN (Electronic)9781728122724
DOIs
Publication statusPublished - máj. 2019
Event7th IEEE/ACM International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2019 - Montreal, Canada
Duration: máj. 28 2019 → …

Publication series

NameProceedings - 2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2019

Conference

Conference7th IEEE/ACM International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2019
CountryCanada
CityMontreal
Period5/28/19 → …

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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  • Cite this

    Ferenc, R., Hegedus, P., Gyimesi, P., Antal, G., Ban, D., & Gyimothy, T. (2019). Challenging machine learning algorithms in predicting vulnerable javascript functions. In Proceedings - 2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2019 (pp. 8-14). [8823747] (Proceedings - 2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RAISE.2019.00010