DroidDissection: A Hybrid Analysis Framework for Android Malware Detection and Analysis A Hybrid Analysis Framework for Android Malware Detection and Analysis
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Abstract
The Android operating system dominates the mobile ecosystem due to its flexibility, large application market, and open-source architecture. However, these same characteristics make Android an attractive platform for attackers who distribute malicious applications, particularly those designed to intercept banking transactions and steal confidential information. Existing security mechanisms mostly rely on either static or dynamic inspection, and these isolated techniques often fail to reveal concealed or runtime-triggered malicious behavior.
In this study, we present DroidDissection, a framework designed specifically for Android malware detection with an emphasis on banking-related threats. The framework combines static code and permission inspection with controlled dynamic execution, enabling deeper observation of behavior that only emerges during runtime. A real malware sample was examined to validate the approach. The experimental results show that the hybrid inspection strategy increases the accuracy of malware identification and helps uncover behaviors that traditional individual methods may overlook. These findings indicate that the proposed framework can strengthen defense mechanisms against evolving cyber threats targeting Android devices.
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