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Widespread growth in Android malwares stimulates security researchers topropose different methods for analyzing and detecting malicious behaviors inapplications. Nevertheless, current solutions are ill-suited to extract thefine-grained behavior of Android applications accurately and efficiently. Inthis paper, we propose ServiceMonitor, a lightweight host-based detectionsystem that dynamically detects malicious applications directly on mobiledevices. ServiceMonitor reconstructs the fine-grained behavior of applicationsbased on a novel systematic system service use analysis technique. Usingproposed system service use perspective enables us to build a statisticalMarkov chain model to represent what and how system services are used to accesssystem resources. Afterwards, we consider built Markov chain in the form of afeature vector and use it to classify the application behavior into eithermalicious or benign using Random Forests classification algorithm.ServiceMonitor outperforms current host-based solutions with evaluating itagainst 4034 malwares and 10024 benign applications and obtaining 96\% ofaccuracy rate and negligible overhead and performance penalty.

Android Malware Detection using Markov Chain Model of Application Behaviors in Requesting System Services