A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Author:

Faruki Parvez1,Bhan Rati2ORCID,Jain Vinesh3,Bhatia Sajal4ORCID,El Madhoun Nour5ORCID,Pamula Rajendra2

Affiliation:

1. Department of Technical Education, Government of Gujarat, Gandhinagar 382010, India

2. Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad 826004, India

3. Department of Computer Science and Engineering, Engineering College Ajmer, Ajmer 305001, India

4. School of Computer Science and Engineering, Sacred Heart University, Fairfield, CO 06825, USA

5. LISITE Laboratory, ISEP, 10 Rue de Vanves, 92130 Issy-les-Moulineaux, France

Abstract

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection.

Publisher

MDPI AG

Subject

Information Systems

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3. Unmasking the Veiled: A Comprehensive Analysis of Android Evasive Malware;Proceedings of the 19th ACM Asia Conference on Computer and Communications Security;2024-07

4. Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection;2024 11th International Conference on Computing for Sustainable Global Development (INDIACom);2024-02-28

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