Learning to detect Android malware via opcode sequences

Pektas A., ACARMAN T.

NEUROCOMPUTING, vol.396, pp.599-608, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 396
  • Publication Date: 2020
  • Doi Number: 10.1016/j.neucom.2018.09.102
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, zbMATH
  • Page Numbers: pp.599-608
  • Keywords: Android malware, Deep learning, Instruction call graph, Neural network, CLASSIFICATION, NETWORK
  • Galatasaray University Affiliated: Yes


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.