关键词: Android平台, 恶意软件, 多特征协作决策, 机器学习

Abstract: With more and more widespread use of smartphones, malwares have become increasingly complex and large-scalely. As a free and open source system, Android has currently surpassed other mobile platforms to become the most popular operating system, so that the number of the Android platform malware has also been significantly increased. Focusing on the security issues of the software for the Android platform, this paper proposes an Android malware detection method based on multifeature collaborative decision. This method mainly bases on the analysis of the Android application, and then the feature attributes are extracted, the models according to machine learning are built. Lastly the classification algorithms are used to determine whether the application is malware. Experimental results show that using the proposed method to classify Android application data set has better assessments indicators than the indicators using other classifiers. Therefore, the method based on multi-feature collaborative decision approach to detect malicious software on Android applications can be made effective for detecting unknown malicious nature of the applications, and can avoid damage caused by malicious applications for the users.

Key words: Android platform, malware, multifeature collaborative decision, machine learning