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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 謝宏昀(Hung-Yun Hsieh) | |
| dc.contributor.author | Guo-Liang Hung | en |
| dc.contributor.author | 洪國喨 | zh_TW |
| dc.date.accessioned | 2022-11-25T03:06:17Z | - |
| dc.date.available | 2022-12-31 | |
| dc.date.copyright | 2021-11-01 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-29 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81905 | - |
| dc.description.abstract | 近年來，無人機 (UAV) 的技術、應用和數量蓬勃發展與增加。隨著無人機執行的任務越來越重要和複雜，無人機執行的任務或無人機本身逐漸成為攻擊者感興趣的目標。儘管大多數無人機十分仰賴全球衛星導航系統 (GNSS) 來獲得正確的座標、導航和時間 (PNT) 訊息，大多數民用 GNSS 訊號並未加密。這種公開性和可預測性使得 GNSS 訊號非常容易偽造。除了討論針對無人機的低成本 GNSS 欺騙與偽造攻擊之外，本文還提出了針對這些攻擊的低成本但有效的偵測和緩解方案。在攻擊方面，我們利用軟體定義無線電 (SDR) 對民用無人機進行 GNSS 欺騙與偽造攻擊，實作了一種低成本但有效的 GNSS 訊號重送攻擊技術。在防禦方面，民用無人機不僅受到 GNSS 天線尺寸、設備結構、訊號處理能力等限制，而且無人機本身的處理器運算能力也極差。因此，我們提出了一種基於機器學習 (ML) 的偵測和緩解方案，本解決方案使用純軟體，並使用各種設計來降低運算複雜度。此機器學習模型通過生成對抗網路 (GAN) 訓練過程，捕捉實際的無人機飛行數據特徵。這個專門設計的模型可以實現實時偵測無人機本身是否受到欺騙與偽造攻擊，並在受到此類攻擊時預測合理的 GNSS 位置資訊，大幅提高了無人機的可靠性。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T03:06:17Z (GMT). No. of bitstreams: 1 U0001-1810202121565700.pdf: 8221970 bytes, checksum: 4de0bc55f8dd8c608c42cf968b02a2ce (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | ABSTRACT ii LIST OF TABLES vi LIST OF FIGURES vii CHAPTER 1 INTRODUCTION 1 CHAPTER 2 BACKGROUND AND RELATED WORK 4 2.1 Global Navigation Satellite Systems (GNSS) 4 2.1.1 Principle of Operation 5 2.1.2 Systems 7 2.1.3 GNSS Jamming 11 2.1.4 GNSS Spoofing 13 2.2 Software-Defined Radio 14 2.3 Machine Learning Techniques 15 2.3.1 Canonical Recurrent Neural Network (RNN) 16 2.3.2 Long Short-Term Memory (LSTM) 17 2.3.3 Gated Recurrent Unit (GRU) 19 2.3.4 Binary Classification 20 2.3.5 AutoEncoder 21 2.3.6 Conditional Generative Adversarial Network (cGAN) 23 2.3.7 Label Smoothing 28 2.3.8 Deep Adaptive Input Normalization 30 2.4 UAVs 32 2.4.1 Characteristics of UAVs 34 2.4.2 DJI Mavic Air 2 35 2.4.3 DatCon 36 2.5 Related Work 38 2.5.1 General Scenario 38 2.5.2 Target on UAV 40 CHAPTER 3 ATTACK IMPLEMENTATION 42 3.1 Setup 42 3.1.1 Software-Defined Radio 43 3.1.2 GNSS Signal Relay 46 3.2 Approaches 47 3.2.1 Forced Landing 47 3.2.2 Transmitting GNSS signal with fixed coordinate 49 3.2.3 Transmitting GNSS signal with shifting coordinate 51 3.3 Observation and Discussion 51 CHAPTER 4 DETECTION AND MITIGATION 59 4.1 Log File Processing 61 4.1.1 Data Fetching 61 4.1.2 Data Parsing and Cleaning 62 4.1.3 Data Analyzing 63 4.1.4 Features 63 4.1.5 Downsampling and Result 68 4.2 Detection 68 4.2.1 RNNs 69 4.2.2 Details of training RNNs 70 4.2.3 Label Smoothing 74 4.2.4 Deep Adaptive Input Normalization 77 4.3 BC-AE-GAN 78 4.3.1 Architecture 79 4.3.2 Scheme 83 CHAPTER 5 PERFORMANCE EVALUATION 90 5.1 Detection 90 5.1.1 RNN Neuron Type 93 5.1.2 Model Size 96 5.1.3 Label Smoothing 99 5.1.4 Deep Adaptive Input Normalization 101 5.2 BC-AE-GAN 104 5.2.1 Skipping AutoEncoder Training Phase 106 5.2.2 Streamlining Output 106 5.2.3 Summary 112 CHAPTER 6 CONCLUSION AND FUTURE WORK 117 REFERENCES 120 | |
| dc.language.iso | en | |
| dc.subject | 生成對抗網路 | zh_TW |
| dc.subject | 全球衛星導航 | zh_TW |
| dc.subject | 無人機 | zh_TW |
| dc.subject | 欺騙 | zh_TW |
| dc.subject | 欺偽 | zh_TW |
| dc.subject | 欺騙偵測 | zh_TW |
| dc.subject | 欺騙緩解 | zh_TW |
| dc.subject | spoofing mitigation | en |
| dc.subject | GAN | en |
| dc.subject | GNSS | en |
| dc.subject | UAV | en |
| dc.subject | spoofing | en |
| dc.subject | spoofing detection | en |
| dc.title | 偵測與緩解針對民用無人機之衛星導航欺偽攻擊 | zh_TW |
| dc.title | Detection and Mitigation of GNSS Spoofing Attack against Civilian UAVs | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 魏宏宇(Hsin-Tsai Liu),李宏毅(Chih-Yang Tseng),高榮鴻 | |
| dc.subject.keyword | 全球衛星導航,無人機,欺騙,欺偽,欺騙偵測,欺騙緩解,生成對抗網路, | zh_TW |
| dc.subject.keyword | GNSS,UAV,spoofing,spoofing detection,spoofing mitigation,GAN, | en |
| dc.relation.page | 127 | |
| dc.identifier.doi | 10.6342/NTU202103848 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-31 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-12-31 | - |
| 顯示於系所單位： | 電機工程學系 | |
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| U0001-1810202121565700.pdf | 8.03 MB | Adobe PDF | 檢視/開啟 |
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