Identifying ATTCK Tactics in Android Malware Control Flow Graph Through Graph Representation Learning and Interpretability

Jeffrey Fairbanks, Andres Orbe, Christine Patterson, Janet Layne, Edoardo Serra, Marion Scheepers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

To mitigate a malware threat it is important to understand the malware's behavior. The MITRE ATTACK ontology specifies an enumeration of tactics, techniques, and procedures (TTP) that characterize malware. However, absent are automated procedures that would characterize, given the malware executable, which part of the execution flow is connected with a specific TTP. This paper is the first in providing an automation methodology to locate TTP in a sub-part of the control flow graph that describes the execution flow of a mal-ware executable. This methodology merges graph representation learning and tools for machine learning explanation.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5602-5608
Number of pages7
ISBN (Electronic)9781665439022
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: 15 Dec 202118 Dec 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online
Period15/12/2118/12/21

Keywords

  • Control Flow Graph
  • Graph Representation Learning
  • Machine Learning Interpretability
  • Malware Tactics Classification

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