Back-propagation neural network on Markov chains from system call sequences: a new approach for detecting Android malware with system call sequences

Abstract

Android has become the most prevalent mobile system, but in the meanwhile malware on this platform is widespread. System call sequences are studied to detect malware. However, malware detection with these approaches relies on common system-call-subsequences. It is not so efficient because it is difficult to decide the appropriate length of the common subsequences. To address this issue, the authors propose a new approach, back-propagation neural network on Markov chains from system call sequences (BMSCS). It treats one system call sequence as a homogeneous stationary Markov chain and applies back-propagation neural network (BPNN) to detect malware by comparing transition probabilities in the chain. Since transition probabilities from one system call to another in malware are significantly different from those in benign applications, BMSCS can efficiently detect malware by capturing the anomaly in state transitions with the help of BPNN. The authors evaluate the performance of BMSCS by experiments with real application samples. The experiment results show that the F -score of BMSCS achieves up to 0.982773, which is higher than the other methods in the literature.