Bayesian Model Updating Method Based Android Malware Detection for IoT Services

摘要

At present, many Internet of Things services are monitored and controlled by smart phone applications. The combination of Internet of Things and smart phones provides users with many convenient services. However, these convenient services also have potential security problems, such as privacy leakage or remote intrusion. Attackers are extending the scope of attacks from existing PC and Internet environments to mobile devices. In this paper, we extract the characteristics of the network traffic generated during the Internet connection, then use the information gain algorithm to select the discriminant classification features, establish the classifier by Bayesian model updating method which is an improved algorithm based on Bayesian theory, and compare with other machine learning classifiers such as k-nearest neighbor (KNN), SVM and J48, improved algorithm has good performance on validity, accuracy, efficiency and strong practicability.