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Using GANs to Improve the Accuracy of Machine Learning Models for Malware Detection

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Intelligent Data Engineering and Automated Learning – IDEAL 2022 (IDEAL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13756))

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Abstract

The increase of cyber-attacks and new malware in the last decade led to the usage of various machine learning techniques in security products. While these techniques are designed to improve accuracy, some practical constraints (such as lowering the false positive rate) often influence the selected model.

This paper focuses on how various generative adversarial networks can be used to improve the average detection rate and reduce the false positives for a given neural network, by altering the training set. The result of this paper is a technique that can be used to reduce the number of false positives while preserving or in some cases increasing the detection rate.

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Notes

  1. 1.

    https://www.av-test.org/en/statistics/malware/.

  2. 2.

    Generative adversarial networks.

  3. 3.

    https://en.wikipedia.org/wiki/RGBA_color_model.

  4. 4.

    Toronto Face Dataset.

  5. 5.

    Randomized rounding Fast Gradient Sign Method.

  6. 6.

    https://msrc-blog.microsoft.com/2020/06/01/machine-learning-security-evasion-competition-2020-invites-researchers-to-defend-and-attack/.

  7. 7.

    A unclear behaviour which is not recognized neither clean nor malware.

  8. 8.

    Application program interface.

  9. 9.

    \(Feat_{value}\) is the numerical value of a feature, \(Feat_{mean}\) represents the average value of that feature across all samples from the database, \(Feat_{stddev}\) is the standard deviation (for the values of that feature).

  10. 10.

    False positive rate.

References

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Authors and Affiliations

  1. Faculty of Computer Science, “Al.I. Cuza” University, Iaşi, Romania

    Ciprian-Alin Simion, Gheorghe Balan & Dragoş Teodor Gavriluţ

  2. Bitdefender Laboratory, Iaşi, Romania

    Ciprian-Alin Simion, Gheorghe Balan & Dragoş Teodor Gavriluţ

Authors
  1. Ciprian-Alin Simion
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  2. Gheorghe Balan
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  3. Dragoş Teodor Gavriluţ
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Corresponding author

Correspondence to Ciprian-Alin Simion .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

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Simion, CA., Balan, G., Gavriluţ, D.T. (2022). Using GANs to Improve the Accuracy of Machine Learning Models for Malware Detection. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_39

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  • DOI: https://doi.org/10.1007/978-3-031-21753-1_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21752-4

  • Online ISBN: 978-3-031-21753-1

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