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研究生:池恩宏
研究生(外文):Chih, En-Hung
論文名稱:IoT網路中多樣加密機制下的惡意流量分類研究
論文名稱(外文):Classifying Malicious Traffic under Various Encryption Schemes in IoT Network
指導教授:温宏斌
指導教授(外文):Wen, Hung-Pin
口試委員:孫宏民黃育綸帥宏翰
口試委員(外文):SUN, HUNG-MINHUANG, YU-LUNSHUAI, HUNG-HAN
口試日期:2023-02-23
學位類別:碩士
校院名稱:國立陽明交通大學
系所名稱:電機工程學系
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:2023
畢業學年度:111
語文別:英文
論文頁數:49
中文關鍵詞:物聯網深度學習加密惡意流量分類網絡安全
外文關鍵詞:IoTdeep learningencrypted malicious traffic classificationcyber security
相關次數:
  • 被引用被引用:0
  • 點閱點閱:42
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Table of Contents
中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
英文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 RELATED WORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 EMT classification by raw data . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 EMT classification by statistical data . . . . . . . . . . . . . . . . . . . . . . . 7
3 SSPP FRAMEWORK FOR EMT CLASSIFICATION . . . . . . . . . . . . . . . 9
3.1 Feature Extraction Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.1 Session-Statistical (SS) vector . . . . . . . . . . . . . . . . . . . . . . 10
3.1.2 Packet-to-Packet (PP) matrix . . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Traffic Prediction module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2.1 Sparse Autoencoder (SAE) . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2.2 1D-CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2.3 Fully Connected Network (FCN) . . . . . . . . . . . . . . . . . . . . . 19
4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.1 Experiment Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.1.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.1.2 Encryption schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.3 Settings of the SSPP framework . . . . . . . . . . . . . . . . . . . . . 23
4.1.4 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 Experiment Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.2.1 Number of extracted packets . . . . . . . . . . . . . . . . . . . . . . . 25
4.2.2 Classification performance with three encrypted datasets . . . . . . . . 27
4.2.3 storage and computational cost . . . . . . . . . . . . . . . . . . . . . . 30
5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
6.1 Number of neurons in the encoding layer of SAE . . . . . . . . . . . . . . . . 39
6.2 Classification performance of only 1D-CNN, only SAE, and SSPP . . . . . . . 40
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
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