Dong-Wook Kim, Kyung-Gi Na, Myung-Mook Han, Mijoo Kim, Woong Go, Jun Hyung Park, Journal of Internet Computing and Services, Vol. 19, No. 1, pp. 27-36, Feb. 2018
10.7472/jksii.2018.19.1.27, Full Text:
Keywords: Android, Behivavr Analysis, feature extraction, Correlation Analysis, Malware Application Classification

Abstract

This paper is a study to classify malicious applications in Android environment. And studying the threat and behavioral analysis of malicious Android applications. In addition, malicious apps classified by machine learning were performed as experiments. Android behavior analysis can use dynamic analysis tools. Through this tool, API Calls, Runtime Log, System Resource, and Network information for the application can be extracted. We redefined the properties extracted for machine learning and evaluated the results of machine learning classification by verifying between the overall features and the main features. The results show that key features have been improved by 1~4% over the full feature set. Especially, SVM classifier improved by 10%. From these results, we found that the application of the key features as a key feature was more effective in the performance of the classification algorithm than in the use of the overall features. It was also identified as important to select meaningful features from the data sets.

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