Android Malware Detection Using Control Flow Graphs and Text Analysis

Ali Muzaffar*, Ahmed Hamza Riaz, Hani Ragab Hassen

*Corresponding author for this work

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

Abstract

The Android OS has a massive user-base with hundreds of millions of devices. Due to the growth of this platform, an increasing number of malicious applications are becoming available to download online. We propose a tool that, when provided a sample application, performs binary classification of the sample as either malicious or benign. We constructed control flow graphs from the API and library calls made by the sample application. We then used control flow graphs to train classification models that utilized text analysis methods such as TF-IDF. Our technique reported accuracy rates of up to 95%.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Applied Cybersecurity (ACS) 2023
EditorsHind Zantout, Hani Ragab Hassen
PublisherSpringer
Pages10-20
Number of pages11
ISBN (Electronic)9783031405983
ISBN (Print)9783031405976
DOIs
Publication statusPublished - 8 Sept 2023
Event2nd International Conference on Applied Cyber Security 2023 - Dubai, United Arab Emirates
Duration: 29 Apr 202329 Apr 2023

Publication series

NameLecture Notes in Networks and Systems
Volume760
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference2nd International Conference on Applied Cyber Security 2023
Abbreviated titleACS 2023
Country/TerritoryUnited Arab Emirates
CityDubai
Period29/04/2329/04/23

Keywords

  • Android malware
  • API calls
  • Control Flow graph

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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