車載通訊網路(VANET, Vehicular Ad Hoc Network)，是利用車輛或道路設施為節點的概念，能讓車與車或車與道路設施之間可以互傳封包。在這基礎下便可以延伸出許多類型之車載系統，行車警報系統便是其中之一，當車輛出現異常情形時，便可以利用行車警報系統機制通知其他車輛。然而，攻擊者可以藉由散發假警報擾亂其他駕駛者，系統也有可能誤發警報的情形。因此，本論文研究駕駛者在高速公路上可能遭遇到的警報情形，制定完善的偵測系統探測行車警報系統的警報真實性，以及在行車警報系統未偵測到該異常車輛時，系統可以輔助偵查。
本研究根據過往文獻針對VANET異常車輛的偵測方式，統整為五大面向，然而，過往的文獻沒有任何一篇可以包含這五大面向。在本研究的系統設計中，即包含這五大面向，使得系統更加完善，攻擊者不易發送假警報擾亂駕駛者。在高速公路的環境中，車輛會發送Beacon資訊，每秒傳送一次車輛的位置等訊息給其他車輛。當接收到事故警報時，偵測系統可以藉由Beacon資訊確認警報真實性。然而，在現實環境中，不一定每輛車皆有車載系統可以傳送Beacon訊息，在過往文獻中，也都假設每輛車皆可發出Beacon資訊，與現實情況不一定相符。因此，本論文主要探究在不同的未發送Beacon資訊的車輛比例下，對於事故車輛的偵測率之影響。
本研究使用車輛叢集的概念，可以減低誤報率，更加確定何者為事故車輛。本研究也與LEAVE演算法相比較，證明本研究誤判率相當低，可以相當準確偵測到事故車輛。

VANET, so called Vehicular Ad hoc Network, uses cars as nodes to send packages to other cars or road infrastructure. In this basis, VANET can extend lots of car system, and one of them is car alarm system. When the car have strange behavior, car alarm system can sand alarm messages to other cars. However, attackers can spread fake alarm messages to disturb other drivers. The system might also have alarm mistake. So, this study researches the possible situation in the highway, and formulate the perfect misbehavior detect scheme (MDS) to verify the alarm messages. When car alarm system can’t detect the misbehavior car, our system also can assist the car alarm system.
According to past studies against to detect misbehavior cars, our study integrate them to five dimensions. However, all of the past studies can’t complete contain five dimensions. This study can contain this five dimensions, so that the system can more complete, attackers can’t easily disturb the drivers. In highway, cars can send Beacon messages, which is sent by every second. The Beacon message contains such as car locations, so that the MDS can use the Beacon messages to verify the alarm message. In the real world, not all the cars can send Beacon messages, however, all the past studies assume all the car can send Beacon messages. It may not correct. Our study major for how the rate of not sending Beacon message cars that influences the MDS detection.
This study use “Car Cluster” to reduce false positive rate, and have more confidence to determine the misbehavior car. This study compares with LEAVE algorithm, proving our scheme have lower false positive rate and have high detect rate.

摘要 i
Abstract ii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 7
1.3 論文架構 8
第二章 文獻探討 9
2.1 物聯網簡介 9
2.2 VANET簡介 10
2.3 MDS簡介與五大面向 13
2.4 探討過往文獻的偵測方法 16
2.5 以五大面向探討過往文獻 19
第三章 系統設計 23
3.1 攻擊模式 24
3.2 VANET模擬環境 25
3.3 車輛叢集 31
3.4 系統流程 34
3.5 慢速車輛偵測情形探討 43
第四章 系統評估 46
4.1 評估方式 46
4.2 模擬環境 48
4.3 LEAVE演算法 51
4.4 實驗簡介 52
4.5 實驗一：初探未發送Beacon車輛比率對於偵測率之影響 53
4.6 實驗二：車輛叢集間距對於偵測率之影響 56
4.7 實驗三：車輛速度對偵測率之影響 59
4.8 實驗四：未發送Beacon車輛比率與密度對於偵測率之影響 61
4.9 實驗五：資料傳送範圍對於偵測率之影響 71
4.10 實驗六：車輛比例對於偵測率之影響 73
第五章 結論 75
參考文獻 77

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