An Android Malware Detection Method Using Multi-Feature and MobileNet

Zhiyao YANG, Xu YANG, Heng ZHANG, Haipeng JIA, Mingliang ZHOU, Qin MAO*, Cheng JI, Xuekai WEI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Most of the existing static analysis-based detection methods adopt one or few types of typical static features for avoiding the problem of dimensionality and computational resource consumption. In order to further improve detecting accuracy with reasonable resource consumption, in this paper, a new Android malware detection model based on multiple features with feature selection method and feature vectorization method are proposed. Feature selection method for each type of features reduces the dimensionality of feature set. Weight-based feature vectorization method for API calls, intent and permission is designed to construct feature vector. Co-occurrence matrix-based vectorization method is proposed to vectorize opcode sequence. To demonstrate the effectiveness of our method, we conducted comprehensive experiments with a total of 30,000 samples. Experimental results show that our method outperforms state-of-the-art methods.

Original languageEnglish
Article number2350299
JournalJournal of Circuits, Systems and Computers
Volume32
Issue number17
Early online date23 May 2023
DOIs
Publication statusPublished - 30 Nov 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 World Scientific Publishing Co. Pte Ltd. All rights reserved.

Keywords

  • Android
  • co-occurrence matrix
  • malware detection
  • vectorizing

Fingerprint

Dive into the research topics of 'An Android Malware Detection Method Using Multi-Feature and MobileNet'. Together they form a unique fingerprint.

Cite this