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研究生:馬詩翔
研究生(外文):MA, SHI-XIANG
論文名稱:使用徑向基底函數網路之 Android 惡意程式分類
論文名稱(外文):Android Malware Classification based on Radial Basis Function Network
指導教授:王正豪王正豪引用關係
指導教授(外文):WANG, JENQ-HAUR
口試委員:劉傳銘楊凱翔
口試委員(外文):LIU, CHUAN-MINGYANG, KAI-HSIANG
口試日期:2018-07-19
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:資訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:107
語文別:中文
論文頁數:32
中文關鍵詞:操作碼N-GramAndroid 惡意程式機器學習靜態分析軟體安全
外文關鍵詞:OpcodeN-GramAndroid malwareMachine learningStatic AnalysisSoftware Security
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N-Gram 是常用於文件檢索的特徵擷取方式,透過 N-Gram 的不同組合可以取得多 樣化的特徵。針對程式的特徵擷取,通常的做法是使用沙盒(Sandbox)模擬執行,建構數 據蒐集環境進行動態分析,擷取系統函數呼叫、執行時期網路與系統資源使用等,且需 要複雜的環境設定。本論文提出使用靜態分析方法,透過 N-Gram 擷取出操作碼(Opcode), 並且加上其出現的頻率來評估 Android 作業系統上的應用程式,比起動態分析不需要執 行程式,在原始檔案即可進行分析。透過計算特徵出現的頻率,並使用資訊增益 (Information Gain)與資訊量增益比例(Information Gain Ratio)兩種特徵選取方法,搭配支 援向量機(SVM)與徑向基底函數網路(Radial basis function network, RBF Net)兩種分類器, 來評估機器學習對於 Android 惡意程式分類之成效。實驗結果顯示,在使用資訊增益搭 配 RBF Net,在 3-Gram 即可達到 G-Mean 0.96 及 MAE 0.0547。因此可以驗證所提出方 法,能有效分類惡意程式。
N-Gram is a feature extraction method commonly used in information retrieval, while it can get lots of different combinations of features. For the feature extraction of the program, the usual practice is to use sandbox for execution simulation and construct a data collection environment for dynamic analysis. It usually captures system function calls, to monitor run- time network and system resource usage, etc..., which needs complex environment settings. This paper proposes to use the static analysis method to extract the opcodes through N-Gram and get the frequency of its occurrence for the application programs in the Android operating system. Compared to the dynamic analysis, the application program doesn’t need to be executed. And can be analyzed in its original form. After calculating the frequency of the opcode feature, we use two feature selection methods information gain and the information gain ratio, with two classifiers support vector machine and radial basis function network to evaluate the effectiveness for Android malware classification. The experimental results show that G-Mean 0.96 and MAE 0.0547 can be achieved in 3-Gram when using the information gain with RBF Net. Therefore, the effectiveness of the proposed method in classifying Android malware can be effectively classified.
摘 要...........................................................................................................................................i
ABSTRACT ............................................................................................................................... ii
誌 謝.........................................................................................................................................iv
目 錄..........................................................................................................................................v
圖目錄 ...................................................................................................................................... vii
表目錄 ..................................................................................................................................... viii
第一章 緒論..............................................................................................................................1
1.1 研究背景與動機 ............................................................................................................. 1
1.2 研究目的 ......................................................................................................................... 1
1.3 研究貢獻 ......................................................................................................................... 2
1.4 章節概要 ......................................................................................................................... 2
第二章 相關研究......................................................................................................................4
2.1 靜態分析 (Static Analysis) ............................................................................................ 4
2.1.1 惡意程式 N-Gram 特徵擷取 ................................................................................... 4
2.1.2 執行檔封裝資訊 ...................................................................................................... 5
2.1.3 操作碼 (Opcode).....................................................................................................5
2.2 動態分析 (Dynamic Analysis)....................................................................................... 6
2.3 資訊增益(InformationGain)..........................................................................................6
2.4 支援向量機(Support Vector Machine)........................................................................... 6
第三章 實驗設計......................................................................................................................7
3.1 Benign Crawler & Checker .............................................................................................. 8
3.2 Feature Extraction ............................................................................................................ 9
3.2.1 APK File ........................................................................................................................ 9
3.2.2 APK Tool..................................................................................................................... 10
3.2.3 Dalvik 操作碼 ............................................................................................................. 11
3.2.4 操作碼N-Gram特徵擷取.........................................................................................11
3.3 Malware Classification................................................................................................... 13
3.3.1 Information Gain Ratio................................................................................................ 14
3.3.2 RBF Net ....................................................................................................................... 15
第四章 實驗方法....................................................................................................................16
4.1 實驗環境 ....................................................................................................................... 16
4.2 實驗平台 ....................................................................................................................... 16
4.3 實驗資料集 ................................................................................................................... 16
4.4 驗證方法 ....................................................................................................................... 17
4.5 實驗一:分類器方法比較...........................................................................................19
4.6 實驗二:特徵選取方法比較 ........................................................................................ 24
4.6 實驗三:比較特徵值...................................................................................................27
第五章 結論............................................................................................................................28
5.1 研究結論....................................................................................................................... 28
5.2 後續研究建議............................................................................................................... 28
參考文獻 .................................................................................................................................. 29
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