近年來，隨著智慧型手機的普及，吸引了眾多的開發者紛紛投入開發應用程式，各式各樣不同種類的應用程式應運而生。根據研究機構Strategy Analytics在2016年第三季統計的最新報告中，Android系統的全球市占率創歷史新高，突破87.5%。也就是說，每10個人中，就有9個人手上拿的是Android手機。由於智慧型手機功能越來越強大，越來越多人將智慧型手機取代個人電腦來使用。每天有數以萬計的應用程式被下載下來，也因為如此，吸引了惡意的開發者，在應用程式內藏有惡意行為，成為駭客攻擊的手法。這些惡意軟體可能會竊取使用者的個人隱私資料，如使用者的姓名，手機號碼，通訊錄等，這些個人隱私資料，一旦遭到惡意軟體竊取並洩漏，輕者可能遭到廣告的騷擾，重者則可能會收到詐騙。另外惡意程式也可能會擅自發送付費簡訊、攔截扣費提醒簡訊等等。因此如何偵測惡意程式也成了一個很大的議題。
惡意軟體的分析主要分為兩種 : 靜態分析及動態分析。靜態分析主要是將應用程式反編譯後，取得程式內部的資訊，透過解析正常及惡意程式內部資訊，提取特徵值，經過處理、篩選找出明顯的惡意特徵值；至於動態分析主要是藉由觸發惡意程式，紀錄並分析其程式執行時的行為，提取惡意程式的特徵。本論文是使用靜態分析的方法，以往的研究中只使用Manifest檔案中的權限來偵測惡意程式，得到的準確率很低。為了增加判斷的準確率，我們將Android的Manifest檔案中其他元件都納入分析，另外使用n-gram語法模型來對smali檔的opcode資訊做分析，篩選出明顯的惡意特徵，最後透過machine learning來判斷是否為惡意程式。我們取得大量的惡意程式樣本及正常的應用程式樣本來進行實驗，以驗證我們所提出的方法可以有效辨別出惡意程式。

In recent years, with the popularity of smart phones, attracting a large number of developers have put into the development of application. A wide variety of application and different types of function came into being. According to the latest report of Strategy Analytics in the third quarter of 2016, the Android global market share hits a record high of 87.5 percent. In other words, nine out of ten smart phone users use Android system. As smart phones become more powerful, more and more people will use smart phones instead of personal computers. Tens of thousands of applications are downloaded every day, and because of this, they have attracted malicious developers, malicious behavior in the application, and hacker attack. These malware applications may steal the user’s personal privacy information, such as the user’s name, mobile phone number, address book and so on. Once these personal privacy information hacked and leaked, minor impact may be harassed by advertising, while major impact may be scam. In addition, malware applications may also send text messages without permission, and intercept reminding text messages. So how to detect malicious programs has become a big issue.
Malware application analysis is divided into two methods, static analysis and dynamic analysis. Static analysis is mainly to disassemble the application, then obtains the information inside the program. By analyzing the internal information of normal and malware applications, extract the features. After preprocessing, find the obvious malicious features. As dynamic analysis is mainly to trigger the malware applications, then record and analyze the behavior of the application execution. Extract the characteristics of malware applications. In this paper, we use static analysis method to detect malware application. The previous research only use the permission inside the Manifest file to detect malware. However, the accuracy is low. In order to increase the accuracy of judgment, we will use not only permission but also the other components inside the Manifest file to analysis. We also use n-gram grammar model to analysis the opcode information inside the smali file. Select the obvious malware features, and finally use different machine learning algorithms to classify the malware application. We have a large number of malware and normal applications samples to verify that our proposed method can effectively classify malware applications.

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