On The (In)Effectiveness of Static Logic Bomb Detector for Android Apps
This work addresses the challenge of detecting sophisticated malware in Android apps for security researchers, though it is incremental as it builds on existing methods.
The study evaluated the scalability and effectiveness of TSOPEN, an open-source static logic bomb scanner for Android apps, on over 500,000 applications, finding it achieves a 0.3% false-positive rate but requires removing 90% of sensitive methods, limiting its practicality for automatic detection.
Android is present in more than 85% of mobile devices, making it a prime target for malware. Malicious code is becoming increasingly sophisticated and relies on logic bombs to hide itself from dynamic analysis. In this paper, we perform a large scale study of TSOPEN, our open-source implementation of the state-of-the-art static logic bomb scanner TRIGGERSCOPE, on more than 500k Android applications. Results indicate that the approach scales. Moreover, we investigate the discrepancies and show that the approach can reach a very low false-positive rate, 0.3%, but at a particular cost, e.g., removing 90% of sensitive methods. Therefore, it might not be realistic to rely on such an approach to automatically detect all logic bombs in large datasets. However, it could be used to speed up the location of malicious code, for instance, while reverse engineering applications. We also present TRIGDB a database of 68 Android applications containing trigger-based behavior as a ground-truth to the research community.