Android malware detection using multivariate time-series technique

Recently, use of smart devices has continued to spread in parallel with their performance improvement. The proliferation of smart devices has led to an emergence of various services such as messengers, SNS and smart banking, and brought convenience in using the services. However, the threat called security vulnerabilities is being faced on the other side. The damages suffered from such a threat are personal information leakage, unreasonable charging, root permission acquisition and so on. In addition, it is said that Android, which is considered as the most vulnerable operating system among the smart devices' operating systems, has the greatest damage of malware codes. Accordingly, this paper proposes a technique to detect malicious codes based on Android devices by using the multivariate time-series analysis. A variety of resource information is integrated into a resource to organize data, and an autoregressive moving average model of the time-series models is used to carry out the modeling. The modeled data is matched with real data to detect malicious codes. The proposed method's validity and excellence is suggested through this experimental result.