A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection

Author:

Batur Şahin CananORCID,Abualigah Laith

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference51 articles.

1. Zou Q, Ni L, Zhang T, Wang Q (2015) Deep learning based feature selection for remote sensing scene classification. IEEE Geosci Remote Sens Lett 12(11):2321–2325

2. Alves H, Fonseca B, Antunes N (2016) Software metrics and security vulnerabilities: dataset and exploratory study. In: 12th European dependable computing conference (EDCC), Gothenburg, Sweden. pp 37–44

3. Williams L (2007) Toward the use of automated static analysis alerts for early identification of vulnerability-and attack-prone components. In: Second ınternational conference on ınternet monitoring and protection, San Jose, CA, USA. pp 18–18

4. Antonios G, Dimitris M, Diomidis S (2018) Vulinoss: a dataset of security vulnerabilities in open-source systems. In: Proceedings of the 15th ınternational conference on mining software repositories. ACM, pp 18–21

5. Koc U, Saadatpanah P, Foster JS, Porter A (2017) Learning a classifier for false positive error reports emitted by static code analysis tools. In: Proceedings of the 1st ACM SIGPLAN ınternational workshop on machine learning and programming languages, MAPL 2017. ACM , New York, NY, USA, pp 35–42

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