Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes

Hamad H. Alsheraifi, Hussain M. Sajwani, Saeed M. Aljaberi, Abdelrahman A. Alblooshi, Ali H. Alhashmi, Saoud A. Sharif, Ernesto Damiani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Gathering the training malware traces is restive and can be a nuisance depending on the type of malware, such as behavioral polymorphism. Generative Adversarial Networks (GANs) are well suited for these issues because they can generate synthetic data that mimic actual data. This treatise sheds detailed and thorough insights into the GAN model implemented to generate a proper training mechanism for binary classification. This paper tested tabular-based and pictorial-based models in multiple trials to determine the better one for classification. Furthermore, multiple ML-based classification techniques, such as ensemble learning, Support Vector Machines (SVMs), and Linear Regression, were tested and recorded on tabular and pictorial GANs. Tabular-wise, the RMSE data collected for Random Forest Tree with the Vanilla LeakyReLU-based GAN provided the optimal classification results. Feature interactions and a biological-inspired activation function were considered for optimizing the model. However, they were only additional tests that were not considered part of the leading paper, as testing quantity was insufficient for definitive evidence of optimization.

Original languageBritish English
Title of host publicationProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6516-6521
Number of pages6
ISBN (Electronic)9781665480451
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
Duration: 17 Dec 202220 Dec 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference2022 IEEE International Conference on Big Data, Big Data 2022
Country/TerritoryJapan
CityOsaka
Period17/12/2220/12/22

Keywords

  • Android Malware Detection
  • Deep Learning
  • Generative Adversarial Networks
  • System Calls

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