Android malware detection based on novel representations of apps

Author

Tiezhu SUN
Nadia DAOUDI
Kevin ALLIX
Jordan SAMHI
Kisub KIM, Singapore Management UniversityFollow
Xin ZHOU, Singapore Management UniversityFollow
Abdoul K. KABORE
Dongsun KIM
David LO, Singapore Management UniversityFollow
Tegawende F. BISSYANDE
Jacques KLEIN

Publication Type

Book Chapter

Publication Date

1-2025

Abstract

In the past decade, advancements in computer vision (CV) and natural language processing (NLP) have been driven significantly by deep representation learning. This progress has made image and text representation learning appealing for applications in fields like malware detection, where deep learning methods can overcome the limitations of traditional hand-crafted feature-based approaches, offering enhanced adaptability to various malware variants. This chapter introduces two novel approaches in malware representation learning that leverage these advancements: DexRay and DexBERT. DexRay employs image-based techniques, transforming DEX file bytecode of apps into grayscale “vector” images. These images are then analyzed using a one-dimensional convolutional neural model to determine the presence of malware. DexBERT, inspired by the BERT language model, processes Smali instructions disassembled from bytecode to generate high-level embedding vectors. These vectors are pivotal for tasks such as malicious code localization and malware detection. Both DexRay and DexBERT have demonstrated significant improvements over traditional machine learning methods in malware detection, particularly in terms of accuracy, efficiency, and adaptability to new malware types. This chapter delves into the methodologies and experimental results of these techniques, highlighting their contributions to the field of malware detection and offering insights into their potential for broader applications in cybersecurity.

Discipline

Information Security | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Malware: Handbook of prevention and detection

Volume

91

Editor

Gritzalis, D., Choo, KK.R., & Patsakis, C.

First Page

197

Last Page

212

ISBN

9783031662454

Identifier

10.1007/978-3-031-66245-4_8

Publisher

Springer

City or Country

Cham

Citation

SUN, Tiezhu; DAOUDI, Nadia; ALLIX, Kevin; SAMHI, Jordan; KIM, Kisub; ZHOU, Xin; KABORE, Abdoul K.; KIM, Dongsun; David LO; BISSYANDE, Tegawende F.; and KLEIN, Jacques. Android malware detection based on novel representations of apps. (2025). Malware: Handbook of prevention and detection. 91, 197-212.
Available at: https://ink.library.smu.edu.sg/sis_research/9863

Additional URL

https://doi.org/10.1007/978-3-031-66245-4_8