臺灣博碩士論文加值系統

隨著通訊技術和人工智慧的快速進步，聯網自駕車(CAVs)發展得越來越快，在有關聯網自駕車的研究當中，智慧型路口管理是一個新起的研究主題，也是一個不可或缺的應用。大多數的智慧型路口管理並沒有考慮到安全層面，然而，車聯網中的無線通道是極有可能被攻擊的，其中最常見的攻擊手法是阻斷服務攻擊。在這篇論文當中，我們提出了探討阻斷服務攻擊下的強健式智慧型路口排程，並且為了讓整個排程更有效率，我們把路口分成四個衝突區來做排程。此演算法利用批次處理來對付阻斷服務攻擊，且當該批車輛中無任何一輛被攻擊時，此演算法應用基於圖的模型以至於可以利用圖論方法來避免死結的產生；另一方面，如果該批車輛中含有車輛被攻擊時，我們有機制可以檢查是否要透過該被攻擊車輛的前一台車輛來阻擋該被攻擊車輛。實驗結果顯示此演算法相比於前人的研究可以達到較好的效率，且有機會可以減少車輛碰撞的數量。

As the rapid advance of communication and artificial intelligence technology, connected and autonomous vehicles (CAVs) evolve faster and faster. Among CAV-related studies, intelligent intersection management is a trending topic and an essential application. Most of studies about intelligent intersection management does not consider security concerns. However, the wireless channels of Vehicular Ad-hoc Networks (VANETs) are very likely attacked. The most common attack is Denial-of-Service (DoS) attack. In this thesis, we propose a robust scheduling algorithm for intelligent intersection management considering DoS attacks, and divide the intersection into four conflict zones for making the scheduling more efficient. This algorithm applies batch processing to address DoS attacks, and also applies the graph-based model so that we can avoid deadlocks by graph theories if the batch does not include the vehicles under DoS attack. On the other hand, if the batch includes the vehicles under DoS attack, we have mechanisms to check whether to block the vehicle under DoS attack by its previous vehicle. The experimental results show that this algorithm can achieve a better efficiency than the previous work and have chances to decrease the amount of collisions.

Acknowledgements ii
Abstract (Chinese) iii
Abstract v
List of Tables ix
List of Figures x
Chapter 1. Introduction 1
1.1 Related Work 2
1.2 Motivation 4
1.3 Our Contributions 5
1.4 Thesis Organization 6
Chapter 2. System Modelling and Problem Formulation 7
2.1 System Modelling 7
2.2 Problem Formulation 10
2.2.1 Scenario 10
2.2.2 Assumptions 11
2.2.3 Given 12
2.2.4 Goal 12
Chapter 3. Proposed Algorithm 15
3.1 Algorithm Overview 15
3.2 Further Screening in DoS Mode 16
3.3 Scheduling and Cycle Removal 19
3.4 Efficiency Optimization 24
3.5 Scheduling and Getting Collision Pairs 27
3.6 Roll-back Checking 29
Chapter 4. Experimental Results 32
4.1 Experimental Setup 32
4.2 Performance Comparisons 33
4.3 Robustness Experiments 37
Chapter 5. Conclusion 40
Bibliography 42

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