The security of smart mobile terminals has been an increasingly important issue in recent years. While there are extensive researches on virus detections for smart mobile terminals, most of them share the same framework of virus detection as that for personal computers, and few of them tackle the problem from the standpoint of detection methodology. In this paper, we propose a behavior-based virus detection method for smart mobile terminals which signals the existence of malicious code through identifying the anomaly of user behaviors. We first propose a model to collect and analyze user behaviors and then present a polynomial time algorithm for the virus detection. Next, we evaluate this algorithm by testing it with two commercial malwares and one malware written by ourselves and show that our algorithm enjoys a high virus detection rate. Finally, we notice that the rate of change of the virus detection rate of the algorithm with respect to thresholds matches the real-world situation of user behaviors, which indicates that the proposed algorithm is feasible.
Study on the Impact of Small Class Teaching on Students' Classroom Learning Behavior: Based on College English Teaching
