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研究生:李 娜
研究生(外文):Shubhranshi Kapoor
論文名稱:基於Transformer架構來理解Android 應用程序中對行為保真度級別之描述
論文名稱(外文):Utilizing Transformer-based Architecture to Understand the Description to Behavior Fidelity Level in Android Applications
指導教授:孫宏民
指導教授(外文):Sun, Hung-Min
口試委員:許富皓黃育綸
口試委員(外文):Hsu, Fu-HauHuang, Yu-Lun
口試日期:2022-07-26
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊安全研究所
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:英文
論文頁數:64
中文關鍵詞:多重標籤文本分類手機應用Android安全危險允許Transformer架構深度學習
外文關鍵詞:Multi-label Text ClassificationMobile ApplicationAndroid SecurityDangerous PermissionsTransformer ArchitectureDeep Learning
相關次數:
  • 被引用被引用:0
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  • 下載下載:22
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隨著智慧型手機的使用在這十年中大幅增加,讓用戶了解手機應用程式中暴
露的數據隱私觀念變得相當重要。手機應用程式收集、使用和共享的有關應用
程式功能和用戶數據訊息提及在數據字段中。由於缺乏隱私意識或對該領域的
了解有限,大多數用戶在下載任何應用程式之前不會閱讀信息。此外,用戶缺
乏有效的方法來查找給定類別中的安全應用。根據之前的研究,分析手機應用
程式的數據有助於評估其權限使用情況。本研究的目的是通過分析手機應用程
式的數據計算,並描述數據行為正確性級別,以確保開發人員描述該應用程式
請求的權限使用之間的差距。在這項研究中,從Google Play 商店收集了126,279
個移動應用程式的數據,並訓練了Transformer 架構的BERT 和XLNet 模型來預
測危險的權限使用。XLNet 和BERT 模型的總體準確率分別為92.4% 和91.0%。
對於這個多重標籤文本分類任務,使用基於深度學習的Text CNN 模型作為基礎
模型,準確率可達到90.6%。預測權限與實際權限之間的相關性用於確保數據正
確性級別。
With the prevalence of smartphones and applications for all aspects of daily
life, it is crucial to make users aware about their data exposed in mobile applications. The information about applications functionality and user data which the mobile app collects, uses and shares is mentioned in the app’s metadata fields. Most users do not read the information before downloading an application due to lack of privacy awareness or their limited knowledge of technology. There is a lack of efficient methods for users to find secure apps in a given category. Previous studies found that analyzing mobile apps’ metadata can help evaluate their permission usage. This research calculates the description to behavior fidelity level by analyzing the metadata of mobile apps to determine the gap between developer's descriptions and the permission usage requested by that application. Metadata of 126,279 mobile applications are collected from Google Play Store and transformer architecture-based BERT and XLNet model are trained to predict dangerous permission usage. XLNet and BERT model give overall accuracy of 92.4% and 91.0% respectively. For this multi-label text classification task, a deep learning based Text CNN model is used as a baseline model, which gives an accuracy of 90.6%. The correlation between the predicted permissions and actual permissions is used to determine the fidelity level.
Abstract (Chinese) I
Abstract II
Acknowledgements III
Contents IV
List of Figures VII
List of Tables IX
1 Introduction 1
2 Background 6
2.1 Android Dangerous Permissions . . . . . . . . . . . . . . . . . . . . 6
2.2 CNN for Text Classification . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Transformer based models . . . . . . . . . . . . . . . . . . . . . . . 9
2.3.1 Bidirectional Encoder Representations from Transformers
(BERT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 XLNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3 Related Work 15
3.1 Android App Metadata . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Description to Behaviour Fidelity . . . . . . . . . . . . . . . . . . . 16
3.3 Deep Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . 17
3.4 Class Imbalance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4 Proposed Methodology 19
4.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.2.1 Description Preprocessing . . . . . . . . . . . . . . . . . . . 26
4.2.2 Permissions Preprocessing . . . . . . . . . . . . . . . . . . . 27
4.3 Tokenization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.3.1 Tokenizer with GloVe word embeddings . . . . . . . . . . . . 29
4.3.2 BERT Tokenizer . . . . . . . . . . . . . . . . . . . . . . . . 29
4.3.3 XLNet Tokenizer . . . . . . . . . . . . . . . . . . . . . . . . 31
4.4 Training the Models . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4.1 Features & Labels . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4.2 Text CNN Model . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4.3 BERT Model . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.4.4 XLNet Model . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.4.5 Handling Class Imbalance . . . . . . . . . . . . . . . . . . . 36
4.5 Fidelity Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5 Experiment result 40
5.1 Experiment Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.2 Performance Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.3 Evaluation on Test data set . . . . . . . . . . . . . . . . . . . . . . 43
5.3.1 Text CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.3.2 BERT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.3.3 XLNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.3.4 Model Comparison . . . . . . . . . . . . . . . . . . . . . . . 49
5.4 Evaluation on AC-Net data set . . . . . . . . . . . . . . . . . . . . 52
5.5 Fidelity Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
6 Conclusion 58
6.1 Limitations & Challenges . . . . . . . . . . . . . . . . . . . . . . . . 59
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Bibliography 60
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