Learning to detect Android malware via opcode sequences


Pektas A., ACARMAN T.

NEUROCOMPUTING, vol.396, pp.599-608, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 396
  • Publication Date: 2020
  • Doi Number: 10.1016/j.neucom.2018.09.102
  • Title of Journal : NEUROCOMPUTING
  • Page Numbers: pp.599-608

Abstract

A large number of Android malware samples can be deployed as the variants of the previously known samples. In consequence, a classification system capable of supporting a large set of samples is required to secure Android platform. Although a large set of variants requires scalability for automatic detection and classification, it also presents a significant advantage about a richer dataset at the stage of discovering underlying malicious activities and extracting representative features. Deep Neural Networks are built by a complex structure of layers whose parameters can be tuned and trained in order to enhance classification statistical metric results. Emerging parallelization computing tools and processors reduce computation time.