Malware Detection in Android Smartphones: A Dynamic Analysis Framework
Android-based smartphones have become a prime target for malware writers due to their widespread use, with numerous malicious applications available on play stores, posing a significant threat to users' personal and business data. Traditional signature-based malware detection schemes are ineffective against new and sophisticated malware, while generic solutions based on static analysis schemes become less effective against obfuscated malware.
To address this challenge, they say VolMemDroid, a dynamic analysis-based framework for detecting malicious Android applications. Our framework extracts volatile memory artifacts for profiling malicious applications using the memory forensic framework of Volatility. They analyze various Volatility plugins for their compatibility with the Android platform and their ability to model application behavior. After testing, chosen plugins are processed for feature extraction, resulting in a comprehensive feature set for Android malware detection and categorization.
Key Findings:
- Effective Detection: VolMemDroid achieves an F1-score of 0.972, outperforming existing volatile memory-based approaches for Android malware detection.
- Accurate Categorization: Our framework effectively categorizes malicious Android applications into four distinct classes.
Conclusion:
VolMemDroid offers a robust and dynamic analysis-based framework for detecting and categorizing malicious Android applications, addressing the limitations of traditional signature-based schemes and static analysis approaches. They claim the framework provides a comprehensive feature set for Android malware detection and categorization, ensuring enhanced protection for users' personal and business data.
VolMemDroid—Investigating android malware insights with volatile memory artifacts
S Khalid, FB Hussain - Expert Systems with Applications, 2024
… malware detection are unable to handle new and sophisticated malware.
Furthermore, generic solutions based on static analysis schemes become less
effective in the presence of obfuscated malware… four malware classes are …
Furthermore, generic solutions based on static analysis schemes become less
effective in the presence of obfuscated malware… four malware classes are …
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