Abstract
In recent years, smart phones become very popular. Lots of people use smart phones instead of traditional phones and almost everyone has one. More and more functional mobile applications are released and it makes user’s life more convenient. The popularity of Android attracts many developers to build not only useful and creative applications, but also some malicious software. Malware installed in user’s smart phone probably not merely steal some privacy information such as phone number, IMEI, credit card number among other but cause some property loss. Therefore, how to detect malicious software on smart phone has become a big issue. In the past, signature-based detection is the most common method to detect malicious software on smart phone. However, the spread of infected malware is faster than researches. Signature-based detection is no longer an effective detect method. In this thesis, we propose An Android behavior-based Malware detection method using Machine learning. We improve an Android application sandbox, Droidbox by inserting a view-identification automatic trigger program which can click mobile applications more effectively. In addition, we collect the behavior such as network activities, file read/write and permission as the feature data and use different machine learning algorithms to classify malware and evaluate the performance. We use a large number of malware and normal application samples to prove that accuracy of our method is pretty high .