博碩士論文 107423054 詳細資訊




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姓名 廖舶凱(Po-Kai Liao)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 Efficient Net結合自動編碼器壓縮模型之Android惡意程式偵測研究
(Efficient Net combined with autoencoder compression model for Android malware detection)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2022-7-31以後開放)
摘要(中) 現今的行動裝置普及,再加上Android作業系統的市占率越來越高,Android惡意程式增長速度越來越快,要如何準確且快速的檢測惡意程式是一個重要的議題。本論文以靜態分析作研究,並且將現今流行的圖像技術應用至Android惡意程式檢測領域中,與現有研究不同的是本研究目標設計出一種有效的分類模型,來解決惡意程式分析上模型的訓練時間冗長的問題。現有圖像惡意程式研究,大多採用VGG Net作為分類器且訓練時間冗長,本研究將自動編碼器(Autoencoder)與圖像領域上使用的深度卷積神經網路(Convolutional Neural Network)結合,運用在惡意程式分析上,旨在縮短訓練時間且達到良好的準確度。自動編碼器(Autoencoder)透過卷積層可以將輸入圖片進行特徵萃取,獲取更低維的向量,此過程可以當作是一種圖像壓縮技術,並提取重要資訊,捨棄不需要的圖像特徵;現今圖像領域中深層卷積模型Efficient Net以較多的卷積層數來獲取圖片更細節特徵,再加上有殘差網路(Residual Network)架構,減少網路退化的問題。本研究採用卷積自動編碼器,並證實可以提取惡意程式特徵將資料集維度縮小,減少訓練時間,並利用Efficient Net作為分類器,在準確度不變的前提下,縮短75%到80%至約500秒的訓練時間。
摘要(英) With the popularity of mobile devices today and the increasing market share of Android operating systems, Android malware is growing faster and faster. How to detect malware accurately and quickly is an important issue. This paper uses static analysis for research, and applies today′s popular image technology to the Android malware detection field. Unlike the existing research, this research goal is to design an effective classification model to solve the problem of lengthy training time and can also improve accuracy. Most of the existing image malware researches use VGG Net as the classifier and they cost lots of time to train. This study combines the Autoencoder and the deep convolutional neural network used in the image field. The malware analysis aims to shorten the training time and achieve good accuracy. Autoencoder can extract feature of input picture through convolutional layer to obtain lower dimensional vector. This process can be regarded as an image compression technology. By extracting important information and discarding unnecessary image features to reduce the dimension. Nowadays in the image field, the deep convolution model Efficient Net uses more convolution layers to obtain more detailed features of the picture, plus a Residual Network architecture to reduce the problem of network degradation. This study uses a convolutional autoencoder and proves that it can extract malware features to reduce the dimension of the data set and reduce training time. under the premise of using different data sets and unchanged accuracy, shorten Up to about 500 seconds of training time.
關鍵字(中) ★ Efficient Net
★ Autoencoder
★ 靜態分析
★ 深度學習
★ Android
關鍵字(英)
論文目次 論文摘要 vi
Abstract vii
目錄 viii
圖目錄 x
表目錄 xii
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機 3
1-3 研究貢獻 6
1-4 章節架構 7
第二章 相關研究 8
2-1 以操作碼為特徵之研究 8
2-2 以自動編碼器為模型之研究 10
2-3 以卷積神經網路為分類模型之研究 13
2-4 小結 16
第三章 系統設計 18
3-1 系統架構 18
3-1-1 資料前處理 19
3-1-2 分類 26
3-1-3 評估指標 28
3-2 系統運作流程 30
第四章 實驗結果 32
4-1 實驗環境與使用資料集 32
4-1-1 實驗設計 32
4-1-2 資料集 33
4-2 探討自動編碼器架構實驗 35
4-2-1 實驗一 35
4-3 與類似研究進行比較 42
4-3-1 實驗二 42
4-4 消融測試 45
4-4-1 實驗三 45
4-5 比較Efficient Net與VGG16之效能 49
4-5-1 實驗四 49
4-6 實驗結果與討論 51
第五章 結論與未來研究 52
5-1 結論與貢獻 52
5-2 未來研究 54
參考文獻 56


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指導教授 陳奕明 審核日期 2020-7-29
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