An Anti-Obfuscation Method for Detecting Similarity Among Android Applications in Large Scale

Code obfuscation exerts a huge impact on similarity detection among Android applications based on behavior characteristics. In order to deal with the situation, we propose a novel way of similarity detection among Android applications based on file content characteristics, which computes the similarity of file content features and can be applied to large-scale scenario in real world. Our method is not subject to code obfuscation or file obfuscation. We choose to utilize the characteristics of image, audio and layout files which are shown in our statistics as the most representative features in Android applications. Meanwhile, different weights are given to these features through machine learning, which further enhances the accuracy of our method. In addition, we implement a prototype system and particularly optimize each step to speed up the calculation, making our system suitable for large-scale scenario and give a good calculation performance. The experiments dataset contains 59 000 applications. And for both legitimate application and malware applications, our system successfully detects those repackaged pirate applications and those with the similar malicious component, which prove the effectiveness of our method. The experiment results demonstrate that similarity detection based on file content characteristics could resist the file obfuscation and give better performance in both accuracy and efficiency.