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Using Machine Learning for Vulnerability Detection and Classification

Authors Tiago Baptista , Nuno Oliveira, Pedro Rangel Henriques



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Author Details

Tiago Baptista
  • Centro Algoritmi, Departamento de Informática, University of Minho, Braga, Portugal
Nuno Oliveira
  • Checkmarx, Braga, Portugal
Pedro Rangel Henriques
  • Centro Algoritmi, Departamento de Informática, University of Minho, Braga, Portugal

Acknowledgements

Special thanks to Search-ON2: Revitalization of HPC infrastructure of UMinho, (NORTE-07-0162-FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2-O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF).

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Tiago Baptista, Nuno Oliveira, and Pedro Rangel Henriques. Using Machine Learning for Vulnerability Detection and Classification. In 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Open Access Series in Informatics (OASIcs), Volume 94, pp. 14:1-14:14, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.SLATE.2021.14

Abstract

The work described in this paper aims at developing a machine learning based tool for automatic identification of vulnerabilities on programs (source, high level code), that uses an abstract syntax tree representation. It is based on FastScan, using code2seq approach. Fastscan is a recently developed system aimed capable of detecting vulnerabilities in source code using machine learning techniques. Nevertheless, FastScan is not able of identifying the vulnerability type. In the presented work the main goal is to go further and develop a method to identify specific types of vulnerabilities. As will be shown, the goal will be achieved by optimizing the model’s hyperparameters, changing the method of preprocessing the input data and developing an architecture that brings together multiple models to predict different specific vulnerabilities. The preliminary results obtained from the training stage, are very promising. The best f1 metric obtained is 93% resulting in a precision of 90% and accuracy of 85%, according to the performed tests and regarding a trained model to predict vulnerabilities of the injection type.

Subject Classification

ACM Subject Classification
  • Security and privacy → Vulnerability scanners
  • Computing methodologies → Machine learning
Keywords
  • Vulnerability Detection
  • Source Code Analysis
  • Machine Learning

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References

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