Abstract
With the advancement of technology and driven by the market, more and more people tend to abandon traditional mobile phone to start using smart phones. In many mobile operating systems, Android because of its openness and becomes the leading consumer market. According to the survey, in 2014, powered by Android operating system equipment shipped over one billion, the number is three times that of Windows, iOS 5 times. Android's popularity not only attracted a large number of developers to develop their creativity to develop new applications, but also attracted a large number of malicious software developers, hoping to get more illegal benefits in Android Market. In fact, many malware mostly comes from existing variant of a malicious software, malware, rather than each of which is an innovative, therefore, the majority of malware can be classified to a family. Malware variants produced, mainly in order to prevent be detected by the anti-virus software, while using some obfuscation technology, such as the random variable is changed to meaningless words, code encryption, an increase of some useless code to enable anti-virus Software does not recognize that this is a malicious software, to infect more people achieve success purpose. This paper use machine learning approach, and to "want to use variables, you must first declare the variables" of this concept, identify each android app Component and permissions to be as the application’s feature, and the experimental data are also display method we use, we can effectively use in the classification of malicious software for each various malware families.