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The Android mobile platform is the most popular and dominates the cell phone market. With the increasing use of Android, malware developers have become active in circumventing security measures by using various obfuscation techniques. The obfuscation techniques are used to hide the malicious code in the Android applications to evade detection by an...
Context in source publication
Context 1
... obfuscation, we need to extract the source code of the application. After extracting the application code, the obfuscation techniques with possible combinations are applied to the APK file. This obfuscated APK may contain malicious code or other malicious activities. Now, this obfuscated APK is analyzed using the anti-malware tools. As shown in Fig. 3, the APK dataset is used to obtain the APK files. After obtaining an APK from the dataset, the first phase is to extract the resource files (i.e., resources, manifests, and other code files) from the APK using APKTools (Apktool, 2022) or another equivalent software program. The extracted manifest.xml contains all configurations for the ...
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Citations
With the rise in popularity and usage of Android operating systems, malicious applications are targeted by applying innovative ways and techniques. Today, malware becomes intelligent that uses several ways of obfuscation techniques to hide its functionality and evade anti-malware engines. For mainstream smartphone users, Android malware poses a severe security danger. An obfuscation approach, however, can produce malware versions that can evade current detection strategies and dramatically lower the detection accuracy. Attempting to identify Android malware obfuscation variations, this paper proposes an approach to address the challenges and issues related to the classification and detection of malicious obfuscated variants. The employed detection and classification scheme uses both static and dynamic analysis using an ensemble voting mechanism. Moreover, this study demonstrates that a small subset of features performs consistently well when they are derived from the basic malware (non-obfuscated), however, after applying a novel feature-based obfuscation approach, the study shows a drastic change indicating the relative importance of these features in obfuscating benign and malware applications. For this purpose, we present a fast, scalable, and accurate mechanism for obfuscated Android malware detection based on the Deep learning algorithm using real and emulator-based platforms. The experiments show that the proposed model detects malware effectively and accurately along with the identification of features that are usually obfuscated by malware attackers.
