Android Malware Detection Using Control Flow Graphs and Text Analysis

Ali Muzaffar; Ahmed Hamza Riaz; Hani Ragab Hassen

doi:10.1007/978-3-031-40598-3_2

Android Malware Detection Using Control Flow Graphs and Text Analysis

Ali Muzaffar^*, Ahmed Hamza Riaz, Hani Ragab Hassen

^*Corresponding author for this work

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the International Conference on Applied Cybersecurity (ACS) 2023
Editors	Hind Zantout, Hani Ragab Hassen
Publisher	Springer
Pages	10-20
Number of pages	11
ISBN (Electronic)	9783031405983
ISBN (Print)	9783031405976
DOIs	https://doi.org/10.1007/978-3-031-40598-3_2
Publication status	Published - 8 Sept 2023
Event	2nd International Conference on Applied Cyber Security 2023 - Dubai, United Arab Emirates Duration: 29 Apr 2023 → 29 Apr 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	760
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	2nd International Conference on Applied Cyber Security 2023
Abbreviated title	ACS 2023
Country/Territory	United Arab Emirates
City	Dubai
Period	29/04/23 → 29/04/23

Keywords

Android malware
API calls
Control Flow graph

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-031-40598-3_2

Cite this

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title = "Android Malware Detection Using Control Flow Graphs and Text Analysis",

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%.",

keywords = "Android malware, API calls, Control Flow graph",

author = "Ali Muzaffar and Riaz, {Ahmed Hamza} and Hassen, {Hani Ragab}",

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Muzaffar, A, Riaz, AH & Hassen, HR 2023, Android Malware Detection Using Control Flow Graphs and Text Analysis. in H Zantout & H Ragab Hassen (eds), Proceedings of the International Conference on Applied Cybersecurity (ACS) 2023. Lecture Notes in Networks and Systems, vol. 760, Springer, pp. 10-20, 2nd International Conference on Applied Cyber Security 2023, Dubai, United Arab Emirates, 29/04/23. https://doi.org/10.1007/978-3-031-40598-3_2

Android Malware Detection Using Control Flow Graphs and Text Analysis. / Muzaffar, Ali; Riaz, Ahmed Hamza; Hassen, Hani Ragab.
Proceedings of the International Conference on Applied Cybersecurity (ACS) 2023. ed. / Hind Zantout; Hani Ragab Hassen. Springer, 2023. p. 10-20 (Lecture Notes in Networks and Systems; Vol. 760).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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