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研究生:林志達
研究生(外文):Chih-ta Lin
論文名稱:惡意程式高維動態行為特徵選取與降維分析
論文名稱(外文):An Efficient Feature Selection and Extraction Analysis for Malware Behavior Classification
指導教授:王乃堅
指導教授(外文):Nai-Jian Wang
口試委員:王乃堅
口試委員(外文):Nai-Jian Wang
口試日期:2015-06-18
學位類別:博士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
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:2015
畢業學年度:103
語文別:英文
論文頁數:114
中文關鍵詞:惡意程式動態分析分類學習數據降維特徵選擇特徵萃取
外文關鍵詞:Dynamic Malware AnalysisData ClassificationDimensionality ReductionFeature SelectionFeature Extraction
相關次數:
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  • 下載下載:62
  • 收藏至我的研究室書目清單書目收藏:1
每年爆量惡意程式(malware)成長驚人持續在網絡和操作系統造成新型態的威脅。傳統利用病毒靜態檔案特徵碼進行已知惡意程式比對偵測,已經無法應付駭客各種大量變種與新型匿藏手法之惡意程式。駭客惡意程式雖然容易迴避掃毒軟體偵測,但其動態程式行為仍充分揭露其意圖特徵,可有效察覺惡意行為,並利用機器學習方式進行預測以提高偵測率。但病毒多樣性與大量行為特徵行為及樣品數,對於惡意程式之分類訓練與學習分析是件耗資源費時的工作。
本論文提出一個通用且有效率的方法來分析預測每一種惡意程式之行為,本文的方法結合特徵選取與萃取方法,在特徵提取階段先選取有效特徵,然後進行特徵降維,二段式方法大量降低特徵空間之維度,然後建立分類學習模型。本研究經過從一沙箱環境紀錄待測程式各項系統、網路及登錄行為紀錄後,以下列五步驟進行特徵降維與分類學習預測:(一)從紀錄檔提取呼叫函式n-gram 特徵文本數據、(二)以SVM 方法建立惡意程式分類學習器、(三)TF-IDF方式選取有效特徵組、(四)以PCA與KPCA等方法進行特徵轉換降維、(五)組合上述步驟建立快速學習與預測模型。
此外,本論文另提出分組特徵選取與轉換方法以提高判斷率,並且在特徵分析過程,本文展示一簡單有效之方式找出各類病毒的主要行為模式。本論文有效收集4,288隻程式涵蓋8類病毒與1類非病毒進行實驗,實驗證明二階段特徵降維可大量減少模型學習分析時間,結合分組TF-IDF、PCA及SVM所建立之分類學習器,可對數十萬高維特徵數據在數十秒內完成分類器之重新學習及預測,實驗結果驗證本文方法之效能與效率具競爭力。
The explosive amount of malware continue their threats in network and operating systems. Signature-based method is widely used for detecting malware. Unfortunately, it is unable to determine variant malware on-the-fly. On the hand, behavior-based method can effectively characterize the behaviors of malware. However, it is time-consuming to train and predict for each specific family of malware.
We propose a generic and efficient algorithm to classify malware. Our method combines the selection and the extraction of features, which significantly reduces the dimensionality of features for training and classification. Based on malware behaviors collected from a sandbox environment, our method proceeds in five steps: (a) extracting n-gram feature space data from behavior logs, (b) building a support vector machine (SVM) classifier for malware classification, (c) selecting a subset of features, (d) transforming high-dimensional feature vectors into low-dimensional feature vectors, and (e) selecting models.
Furthermore, we propose a Multi-Grouping algorithm for each feature reduction method. During the feature selection and extraction process, we show a easy way to figure out the major behaviors for each malware type. Experiments were conducted on a real-world data set with 4,288 samples from 9 families. As a proof of concept, we have evaluated our method by online training simulation experiment. Our 2-stages dimensionality reduction approach could have reduced the time cost significantly. The combination of MG TF-IDF, PCA and SVM for online training can finish the re-training and classifying in seconds, is sufficient to meet the online learning requirement for collecting the malware behavior in every minute. The experiments were demonstrated the effectiveness and the efficiency of our approach.
書名頁... i
中文摘要... ii
英文摘要... iii
誌謝... iv
目次... v
表目次... vii
圖目次... ix
名詞與符號說明... x
1、INTRODUCTION... 1
2、RELATED WORK... 3
3、METHODOLOGY... 7
3.1 Behavior Monitoring and Data Preprocessing... 8
3.2 Training and Testing... 9
3.3 Feature Selection Analysis... 11
3.4 Feature Extraction Analysis... 13
3.4.1 Principal Component Analysis... 13
3.4.2 Kernel Principal Component Analysis... 16
3.5 Model Selection and Online Extension... 17
4、EXPERIMENT ... 23
4.1 Behavior Monitoring and Data Preprocessing ... 23
4.2 Feature Selection Analysis ... 28
4.3 Feature Extraction Analysis ... 38
4.4 Model Selection and Online Extension ... 45
5、CONCLUSIONS ... 56
參考文獻... 57
附錄A: PCA analysis result in the bigram to four-gram test ... 64
附錄B: The first principal component of MG PCA analysis result in the bigram to four-gram test ... 69
附錄C: MATLAB / Machine Learning 程式列表... 81
附錄D: MATLAB / 特徵提取程式列表... 93
附錄E: MATLAB / Online Simulation 程式列表... 105
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