On behavior-based detection of malware on Android platform
Wei Yu, Hanlin Zhang, Linqiang Ge, Rommie L. Hardy · 2013
Because of exponential growth in smart mobile devices, malware attacks on smart mobile devices have been growing and pose serious threats to mobile device users. To address this issue, we develop a malware detection system, which uses a behavior-based detection approach to deal with the detection of a large number of unknown malware. To accurately detect malware, we examine system calls to capture the runtime behavior of software, which interacts with an operating system and adopt machine learning approaches such as Support Vector Machine (SVM) and Naive Bayes learning schemes to learn the dynamic behavior of software execution. Using real-world malware and benign samples, we conduct experiments on Android devices and evaluate the effectiveness of our developed system in terms of learning algorithms, the size of training set, the length of n-grams, and the overhead in training and detection processes. Our experimental data demonstrates the effectiveness of our proposed detection system to detect malware.