Selecting Critical Data Flows in Android Applications for Abnormal Behavior Detection

Abstract

Nowadays, mobile devices are widely used to store and process user privacy and confidential data. With the popularity of Android platform, the cases of attacks against users' privacy-sensitive data within Android applications are on the rise. Researchers have developed sophisticated static and dynamic analysis tools to detect information leakage. These methods cannot distinguish legitimate usage of sensitive data in benign apps from the intentional sensitive data leakages in malicious apps. Recently, malicious apps have been found to treat sensitive data differently from benign apps. These differences can be used to flag malicious apps based on their abnormal data flows. In this paper, we further find that some sensitive data flows show great difference between benign apps and malware. We can use these differences to select critical data flows. These critical flows can guide the identification of malware based on the abnormal usage of sensitive data. We present SCDFLOW, a tool that automatically selects critical data flows within Android applications and takes these critical flows as feature for abnormal behavior detection. Compared with MUDFLOW, SCDFLOW increases the true positive rate of malware detection by 5.73%9.07% on different datasets and causes an ignorable effect on memory consumption.