Directional Graph Transformer-Based Control Flow Embedding for Malware Classification

Hyung Jun Moon, Seok Jun Bu, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Considering the fatality of malware attacks, the data-driven approach using massive malware observations has been verified. Deep learning-based approaches to learn the unified features by exploiting the local and sequential nature of control flow graph achieved the best performance. However, only considering local and sequential information from graph-based malware representation is not enough to model the semantics, such as structural and functional nature of malware. In this paper, functional nature are combined to the control flow graph by adding opcodes, and structural nature is embedded through DeepWalk algorithm. Subsequently, we propose the transformer-based malware control flow embedding to overcome the difficulty in modeling the long-term control flow and to selectively learn the code embeddings. Extensive experiments achieved performance improvement compared to the latest deep learning-based graph embedding methods, and in a 37.50% improvement in recall was confirmed for the Simda botnet attack.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings
EditorsDavid Camacho, Peter Tino, Richard Allmendinger, Hujun Yin, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030916077
Publication statusPublished - 2021
Event22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021 - Virtual, Online
Duration: 2021 Nov 252021 Nov 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13113 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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