PlausMal-GAN: Plausible Malware Training Based on Generative Adversarial Networks for Analogous Zero-day Malware Detection

Dong Ok Won, Yong Nam Jang, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Zero-day malicious software (malware) refers to a previously unknown or newly discovered software vulnerability. The fundamental objective of this paper is to enhance detection for analogous zero-day malware by efficient learning to plausible generated data. To detect zero-day malware, we proposed a malware training framework based on the generated analogous malware data using generative adversarial networks (PlausMal-GAN). Thus, the PlausMal-GAN can suitably produce analogous zero-day malware images with high quality and high diversity from the existing malware data. The discriminator, as a detector, learns various malware features using both real and generated malware images. In terms of performance, the proposed framework showed higher and more stable performances for the analogous zero-day malware images, which can be assumed to be analogous zero-day malware data. We obtained reliable accuracy performances in the proposed PlausMal-GAN framework with representative GAN models (i.e., deep convolutional GAN, least-squares GAN, Wasserstein GAN with gradient penalty, and evolutionary GAN). These results indicate that the use of the proposed framework is beneficial for the detection and prediction of numerous and analogous zero-day malware data from noted malware when developing and updating malware detection systems.

Original languageEnglish
JournalIEEE Transactions on Emerging Topics in Computing
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Analogous Malware Detection
  • Big Data
  • Generative Adversarial Networks
  • Generative adversarial networks
  • Generators
  • Linear programming
  • Malware
  • Malware Augmentation
  • Malware Data
  • Training
  • Training data
  • Zero-day Malware

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications

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