無線隨意網路(Mobile Ad Hoc Networks, MANETs)是由一群行動裝置節點所形成的無線網路，網路節點之間不需要固定的網路基礎設備即可建立通訊。在無線隨意網路下，網路的基本功能與服務由節點之間合作提供。然而這種即時建立的無線網路並沒有任何單位可強制節點之間必須提供可靠的服務，或提供正確的網路資訊，因此任何能夠存取網路的節點皆可不遵守網路通訊協定進行惡意行為，以破壞網路連結性或降低網路服務品質。
本論文提出了一些機制，用來偵測網路節點是否執行惡意行為，並破壞網路的服務品質。首先，這篇論文提出了一個惡意節點模糊推論引擎。此推論引擎根據鄰近節點的行為表現，來推論其他節點是否存在惡意行為。本論文亦提出了一個節點名譽系 (Reputation System)用來評鑑節點的行為表現，並推論網路是否存在惡意節點。此名譽系統根據網路環境動態調整信任評估模組，以提高惡意節點的偵測率與降低惡意節點的誤判率。網路節點的移動與不穩定的無線網路環境是造成惡意行為誤判的元兇。因此本論文亦提出了一個節點名譽校正機制(Reputation Calibration Mechanism)，其根據網路狀況來校正節點的名譽，使一般的名譽系統皆可有效的在不穩定的網路環境下，有效的偵測惡意節點。本論文所提出的惡意節點偵測機制，皆經過大量的網路實驗測試。網路實驗結果證明本論文所提出的節點名譽校正機制，可在高移動性網路與不穩定的網路通訊下有效的偵測並隔離惡意節點，使網路運作正常。

A mobile ad hoc network (MANET) is a wireless communication network formed by a group of mobile devices. In a MANET environment, fixed infrastructure is not supported. When two nodes want to establish communication channel between each other, they require other intermediate nodes, which may move themselves during the communication session, to cooperatively help them to dynamically construct a route. There is no central authority in a MANET, therefore, a node cannot monitor and enforce other nodes to cooperatively provide reliable communication services. Any intermediate node in a data routing path may arbitrarily decide what action it will perform when receiving a route request or a forwarding data packet. Hence, for selfish or malicious reasons, malicious actions such as packet dropping and false information dissemination may be performed by a mobile node easily. Therefore, how to dynamically detect malicious nodes such that normal communications will not be disrupted or delayed and false information will not be spread, has become a critical issue and a challenging research topic in MANETs.
This dissertation proposed several mechanisms based on different concepts to detect malicious nodes in MANETs. First of all, a fuzzy inference engine for nodes in a MANET is proposed. The engine installed inside a node can infer the trust level of a target node based on observing reports from its neighboring nodes. Secondly, a node reputation system, which dynamically changes its trust evaluation models based on the current status of MANET environment, is introduced. Finally, a reputation calibration mechanism for general reputation systems is derived. The most challenge issue of malicious node detection in MANETs is that the dynamics of node mobility and everlastingly changed network status make trust evaluation of a target node inaccurate. The proposed reputation calibration mechanism can correct inaccurate trust value and let reputation system effectively detect malicious nodes in error-prone networks. The proposed mechanisms are all extensively evaluated by network simulations. The simulation results show that the reputation calibration mechanism is a promising way to detect malicious nodes in highly mobile and unstable networks.
The lesson we learned is that there is no clear rule to define whether a detected node behavior is based on malicious motivation in MANETs. Using fixed rules to detect malicious node behaviors is not always suitable. Instead, by adopting calibration mechanism, we can easily detect whether a node behavior is more toward to misbehaved direction or not. Dynamically constructing detection rules based on the behaviors of neighboring nodes is a promising way to effectively detect malicious nodes.

中文摘要I
AbstractIII
誌 謝V
ContentsVI
List of FiguresVIII
List of TablesXI
Chapter 1Introduction1
Chapter 2Related Works8
2.1Credit-based Schemes10
2.2Acknowledgement-based Schemes13
2.3Reputation-based Schemes15
Chapter 3Problem Definition23
Chapter 4Threshold-adaptive Mechanism for Reputation System24
4.1Introduction of Threshold-adaptive Mechanism24
4.2Performance Evaluation36
4.3Brief Summary42
Chapter 5A Dual-core Adaptive Mechanism for Reputation System44
5.1Introduction of Dual-core Adaptive Mechanism45
5.2Performance Evaluation55
5.3Brief Summary65
Chapter 6Calibration Mechanism for Reputation System67
6.1Introduction of Calibration Mechanism68
6.2Implementation of the Calibration Mechanism in a Reputation System72
6.3Performance Evaluations under Different Mobility75
6.4Performance Evaluations under Noise Area92
6.5Discussion on Calibration Mechanism113
6.6Brief Summary117
Chapter 7Conclusion and Future Work119

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