Android malware dynamic evasions
- Citation Author(s):
-
Hayyan Hasan (MDSE Research Group, Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran)Hasan Deeb (Department of Software Engineering and Information Systems, Faculty of Informatics Engineering, Albaath university, Homs, Syria )Behrouz Tork Ladani (MDSE Research Group, Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran)Bahman Zamani (MDSE Research Group, Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran)
- Submitted by:
- hayyan hasan
- Last updated:
- DOI:
- 10.21227/njbg-3c03
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Abstract
This dataset is a hand noted dataset that consists of two categories, evasion and normal methods. By evasion methods we mean the methods that are used by Android malware to hide their malicious payload, and hinder the dynamic analysis. Normal methods are any other methods that cannot be used as evasion techniques. Also, the evasion methods are categorized into six categories: File access, Integrity check, Location, SMS, Time, Anti-emulation. This dataset can be used by any ML or DL approaches to predict new evasion techniques that can be used by malware to hinder the dynamic analysis.
Instructions:
This dataset can be used by ML or DL approaches to predict new evasion techniques that are used by Android malware.
In order to do so, the Android API methods are needed
