רוברט מושקוביץ'

Due to the increasing growth in the variety of Android malware, it is important to distinguish between the unique types of each. In this paper, we introduce the use of a decompiled source code for malicious code classification. This decompiled source code provides deeper analysis opportunities and understanding of the nature of malware. Malicious code differs from text due to syntax rules of compilers and the effort of attackers to evade potential detection. Hence, we adapt Natural Language Processing-based techniques under some constraints for malicious code classification. First, the proposed methodology decompiles the Android Package Kit files, then API calls, keywords, and non-obfuscated tokens are extracted from the source code and categorized to stop-tokens, feature-tokens, and long-tail-tokens. We also introduce the use of generalized N-tokens to represent tokens that are typically less frequent. Our approach was evaluated, in comparison to the use of API calls and permissions for features, as a baseline, and their combination, as well as in comparison to the use of neural network architectures based on decompiled Android Package Kits. A rigorous evaluation of comprehensive public real-world Android malware datasets, including 24,553 apps that were categorized to 71 families for the malicious families classification, and 60,000 apps for malicious code detection was performed. Our approach outperformed the baselines in both tasks.