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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林忠緯(Chung-Wei Lin) | |
dc.contributor.author | Sing-Yao Wu | en |
dc.contributor.author | 吳星耀 | zh_TW |
dc.date.accessioned | 2021-06-16T06:43:54Z | - |
dc.date.available | 2022-08-01 | |
dc.date.copyright | 2020-08-07 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-07-27 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57383 | - |
dc.description.abstract | 協同式自適應巡航控制系統(CACC)為先進駕駛輔助系統(ADAS)中的一環,旨在提升車子跟車時的安全、效率及車串的穩定性。協同式自適應巡航控制系統利用車上安裝的感測器及車聯網的訊息交換,獲得諸如前車的位置、速度、加速度等資訊,藉此更加安全的控制與前車的車距。然而,這些由感測器與車聯網所獲得的資訊具有被攻擊者侵入的風險,一旦遭受侵入,攻擊者便可透過修改這些資訊使系統無法準確保持車距,造成損害。 為了應對這個問題,本研究我們考慮三種針對CACC的攻擊:脈衝攻擊、線性攻擊與隱匿攻擊。其中,我們特別設計了隱匿攻擊來欺騙一個規則式偵測法。接著,我們提出三個不需要攻擊模型作訓練且基於深度學習的侵入偵測方法:預測式偵測法、編碼解碼式偵測法與對抗生成網路式偵測法來檢測協同式自適應巡航控制系統的輸入是否被惡意修改。實驗結果展現了各偵測方法的優勢與劣勢,且顯示在偵測所考慮的攻擊中,對抗生成網路式偵測法的偵測能力最穩定。 | zh_TW |
dc.description.abstract | Cooperative Adaptive Cruise Control (CACC) is an advanced system to improve safety, efficiency, and string stability for a vehicle following its leading vehicle. The vehicle equipped with CACC utilizes vehicular communication and receives information such as the position, velocity, acceleration, or even intention from the leading vehicle. However, it has been shown that sensors and wireless channels are vulnerable to security attacks, and attackers can modify data sensed from sensors or sent from other vehicles. To address this problem, in this thesis, we consider three types of attacks, pulse attacks, linear attacks, and stealthy attacks, on CACC inputs. Especially, we design the stealthy attacks to deceive a rule-based approach. We then develop three deep-learning models, a predictor-based model, an encoder-decoder-based model, and a Generative Adversarial Network-based (GAN-based) model, to detect the attacks, where the three models do not need attacker models for training. We further apply dynamic thresholding for error measure to improve the detection performance. The experimental results demonstrate the respective strengths of different models and suggest that the GAN-based model has the most stable performance of detecting the attacks. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T06:43:54Z (GMT). No. of bitstreams: 1 U0001-2107202016054900.pdf: 2495038 bytes, checksum: f9217e225ad23e159360ca7d3c40038f (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | Acknowledgements ii Abstract (Chinese) iii Abstract v List of Tables ix List of Figures x Chapter 1. Introduction 1 1.1 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Chapter 2. Background and Related Work 6 2.1 Cooperative Adaptive Cruise Control (CACC) . . . . . . . . . . . . . . . 6 2.1.1 Basic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2 Security Issues and Attacks . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Anomaly and Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Data-Centric Intrusion Detection Approaches to Vehicles . . . . . . 9 2.2.2 Survey on Deep Learning-Based Anomaly Detection . . . . . . . . 11 Chapter 3. Problem Formulation 14 3.1 Attacker Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.1 Pulse Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1.2 Linear Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1.3 Stealthy Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2 Detection Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 Evaluation Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Chapter 4. Proposed Approaches 24 4.1 Model 1: Predictor-Based Model . . . . . . . . . . . . . . . . . . . . . . . 24 4.2 Model 2: Encoder-Decoder-Based Model . . . . . . . . . . . . . . . . . . 26 4.3 Error Measure 1: Fixed Thresholding . . . . . . . . . . . . . . . . . . . . 28 4.4 Error Measure 2: Dynamic Thresholding . . . . . . . . . . . . . . . . . . 28 4.5 Model 3: GAN-Based Model . . . . . . . . . . . . . . . . . . . . . . . . . 30 Chapter 5. Experimental Results 33 5.1 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.1.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.1.2 Implementation of Approaches . . . . . . . . . . . . . . . . . . . . 33 5.2 Preliminary Experiment (without False data) . . . . . . . . . . . . . . . . 36 5.3 Pulse Attack Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 5.3.1 Experiment on Different Pulse Offsets . . . . . . . . . . . . . . . . 41 5.3.2 Experiment on Different Pulse Periods . . . . . . . . . . . . . . . . 43 5.4 Linear Attack Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.4.1 Experiment on Different Deviation Rates . . . . . . . . . . . . . . 50 5.5 Stealthy Attack Experiment . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.6 Sensor Noise Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Chapter 6. Conclusions 60 Bibliography 63 | |
dc.language.iso | en | |
dc.title | 基於深度學習的協同式自適應巡航控制系統侵入偵測 | zh_TW |
dc.title | Deep-Learning-Based Intrusion Detection for Cooperative Adaptive Cruise Control | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 施吉昇(Chi-Sheng Shih),蕭旭君(Hsu-Chun Hsiao),陳尚澤(Shang-Tse Chen) | |
dc.subject.keyword | 協同式自適應巡航控制系統,車聯網,異常偵測,侵入偵測,機器學習,深度學習, | zh_TW |
dc.subject.keyword | Cooperative Adaptive Cruise Control (CACC),VANET,Anomaly Detection,Intrusion Detection,Machine Learning,Deep Learning, | en |
dc.relation.page | 66 | |
dc.identifier.doi | 10.6342/NTU202001697 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-07-28 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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