Detecting malicious android applications based on API calls and permissions using machine learning algorithms

Seif El Dein Mohamed; Mostafa Ashaf; Amr Ehab; Omar Shereef; Haytham Metwaie; Eslam Amer

doi:10.1109/MIUCC52538.2021.9447594

Detecting malicious android applications based on API calls and permissions using machine learning algorithms

Seif El Dein Mohamed, Mostafa Ashaf, Amr Ehab, Omar Shereef, Haytham Metwaie, Eslam Amer

School of Computing

Misr International University

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

Abstract

Android malware is growing, and the Android operating system is becoming more mainstream. Malware developers are using new strategies to build harmful Android apps, significantly weakening the capability of conventional malware detectors, which are unable to identify these mysterious malicious applications. Machine learning methods can be used to identify unknown Android malware using the functionality gleaned from static and dynamic reviews of Android apps. This article aims to compare and analyze different Android malware detection systems based on detection techniques, analysis processes, and extracted features. We learned scientific investigations in all Android malware detection approaches that use machine learning, demonstrating that machine learning algorithms are often used in this area to identify Malicious programs in the wild.

Original language	English
Title of host publication	2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021
Editors	Ayman Bahaa-Eldin, Ashraf AbdelRaouf, Nada Ayman Mostafa Shorim, Randa Osama Mohamed Rashad, Shereen Essam Elbohy
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	223-228
Number of pages	6
ISBN (Electronic)	9781665412438
ISBN (Print)	9781665429535
DOIs	https://doi.org/10.1109/MIUCC52538.2021.9447594
Publication status	Published - 9 Jun 2021
Event	2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021 - Cairo, Egypt Duration: 26 May 2021 → 27 May 2021

Conference

Conference	2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021
Country/Territory	Egypt
City	Cairo
Period	26/05/21 → 27/05/21

Keywords

Machine learning algorithms
Operating systems
Machine learning
Detectors
Feature extraction
Ubiquitous computing
Malware

Access to Document

10.1109/MIUCC52538.2021.9447594

Cite this

Mohamed, S. E. D., Ashaf, M., Ehab, A., Shereef, O., Metwaie, H., & Amer, E. (2021). Detecting malicious android applications based on API calls and permissions using machine learning algorithms. In A. Bahaa-Eldin, A. AbdelRaouf, N. A. M. Shorim, R. Osama Mohamed Rashad, & S. E. Elbohy (Eds.), 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021 (pp. 223-228). Article 9447594 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MIUCC52538.2021.9447594

Mohamed, Seif El Dein ; Ashaf, Mostafa ; Ehab, Amr et al. / Detecting malicious android applications based on API calls and permissions using machine learning algorithms. 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021. editor / Ayman Bahaa-Eldin ; Ashraf AbdelRaouf ; Nada Ayman Mostafa Shorim ; Randa Osama Mohamed Rashad ; Shereen Essam Elbohy. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 223-228

@inproceedings{5623d6ced50840589932d5a8e418d2b0,

title = "Detecting malicious android applications based on API calls and permissions using machine learning algorithms",

abstract = "Android malware is growing, and the Android operating system is becoming more mainstream. Malware developers are using new strategies to build harmful Android apps, significantly weakening the capability of conventional malware detectors, which are unable to identify these mysterious malicious applications. Machine learning methods can be used to identify unknown Android malware using the functionality gleaned from static and dynamic reviews of Android apps. This article aims to compare and analyze different Android malware detection systems based on detection techniques, analysis processes, and extracted features. We learned scientific investigations in all Android malware detection approaches that use machine learning, demonstrating that machine learning algorithms are often used in this area to identify Malicious programs in the wild.",

keywords = "Machine learning algorithms, Operating systems, Machine learning, Detectors, Feature extraction, Ubiquitous computing, Malware",

author = "Mohamed, {Seif El Dein} and Mostafa Ashaf and Amr Ehab and Omar Shereef and Haytham Metwaie and Eslam Amer",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE. Noissn.; 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021 ; Conference date: 26-05-2021 Through 27-05-2021",

year = "2021",

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doi = "10.1109/MIUCC52538.2021.9447594",

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Mohamed, SED, Ashaf, M, Ehab, A, Shereef, O, Metwaie, H & Amer, E 2021, Detecting malicious android applications based on API calls and permissions using machine learning algorithms. in A Bahaa-Eldin, A AbdelRaouf, NAM Shorim, R Osama Mohamed Rashad & SE Elbohy (eds), 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021., 9447594, Institute of Electrical and Electronics Engineers Inc., pp. 223-228, 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021, Cairo, Egypt, 26/05/21. https://doi.org/10.1109/MIUCC52538.2021.9447594

Detecting malicious android applications based on API calls and permissions using machine learning algorithms. / Mohamed, Seif El Dein; Ashaf, Mostafa; Ehab, Amr et al.
2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021. ed. / Ayman Bahaa-Eldin; Ashraf AbdelRaouf; Nada Ayman Mostafa Shorim; Randa Osama Mohamed Rashad; Shereen Essam Elbohy. Institute of Electrical and Electronics Engineers Inc., 2021. p. 223-228 9447594.

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

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AB - Android malware is growing, and the Android operating system is becoming more mainstream. Malware developers are using new strategies to build harmful Android apps, significantly weakening the capability of conventional malware detectors, which are unable to identify these mysterious malicious applications. Machine learning methods can be used to identify unknown Android malware using the functionality gleaned from static and dynamic reviews of Android apps. This article aims to compare and analyze different Android malware detection systems based on detection techniques, analysis processes, and extracted features. We learned scientific investigations in all Android malware detection approaches that use machine learning, demonstrating that machine learning algorithms are often used in this area to identify Malicious programs in the wild.

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Mohamed SED, Ashaf M, Ehab A, Shereef O, Metwaie H, Amer E. Detecting malicious android applications based on API calls and permissions using machine learning algorithms. In Bahaa-Eldin A, AbdelRaouf A, Shorim NAM, Osama Mohamed Rashad R, Elbohy SE, editors, 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 223-228. 9447594 doi: 10.1109/MIUCC52538.2021.9447594

Detecting malicious android applications based on API calls and permissions using machine learning algorithms

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Conference

Keywords

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