| Summary: | 碩士 === 國立嘉義大學 === 資訊工程學系研究所 === 106 === Nowadays, there are lots of functions on the smart phones, and it is necessary for people to use smart phones in daily life. Android is the most popular system of smart phones with lots of users. Since the Android system has flexible usage of file control, the users can easily install apps from unverified sources, but the malwares can also threat users by this way.
In this thesis, we present an Android malware analysis system. This system is based on the machine learning technology, and we use the result of dynamic monitoring information and static analysis as features. According to the results by the machine learning, we can determine if the application is malware or not.
In the part of dynamic analysis, we collect the dynamic messages in real time based on Taintdroid. We use an automatic behavior trigger that makes our experiment closer to the user’s actual situation. Combining the dynamic and static analysis data sets, we perform the machine learning to proceed with classification. The results show that our system can distinguish malware from apps with high accuracy rate.
|
|---|
