近年來，物聯網和無線感測器網路的市場持續成長。在2023年，物聯網的市場成長率為16%、無線傳感器的市場成長率為18%。因此，物聯網網路上的安全問題需要受到重視。然而，由於物聯網網路通常涉及節點之間的協作，所以對其他節點的信任評估相當重要。本研究引入了零信任的觀點，會持續對鄰近節點做信任評估，以避免攻擊者危害整體網路的可用性和可靠性。在零信任中，不存在隱性信任，也就是會對各節點的行為做信任評估，以避免其他節點被感染時，導致整體網路受到損害。本研究針對用於低功耗和有損網路協定 (Routing Protocol for Low-Power and Lossy Networks, RPL) 上的黑洞攻擊、拒絕服務攻擊、等級攻擊、版本號攻擊進行信任評估，以防止這4種攻擊者對整體網路造成的損害。本研究透過所提供的各個指標，包含能力 (Capability)、特徵 (Feature)、活動 (Activity)、系統設置 (Data of system information)來對各個交互的行為做信任評估。在檢測黑洞攻擊中，由於每個節點會配合其祖父節點同時對父節點做信任評估，因此不會像其他方法出現很高的誤報率，而導致整體網路的拓樸性能下降。另外，本研究在判斷行為時，是透過該交互節點的能力、預期該有的行為來做評估。因此，提出的方法較不容易出現誤判。

The market for Internet of Things (IoT) and Wireless Sensor Network (WSN) has continued to grow in recent years. In 2023, the market growth rate of IoT is 16%, and the market growth rate of WSN is 18%. Therefore, security issues on IoT Network are considerably important. However, since IoT Network usually involves collaboration among nodes, trust evaluation of the other nodes is crucial. This study introduces the concept of Zero Trust (ZT), which will continuously evaluate the trust of neighbor nodes to prevent attackers from endangering the availability and reliability of the overall network. In zero trust, there is no implicit trust, that is, the behavior of each node will be evaluated, so as to avoid damage to the overall network when other nodes are infected. This study conducts trust evaluation on BlackHole Attack, Denial of Service attack, Rank Attack, and Version Attack on Routing Protocol for Low-Power and Lossy Networks (RPL), in order to prevent these four kinds of attackers from causing damage to the overall network. This research uses various indicators to evaluate the trust of each interaction behavior, including Capability, Feature, Activity and Data. Other methods usually lead to a decrease in the topology performance of the overall network under Blackhole Attack because of their high false positive rate. However, since each node cooperates with its grandparent node to conduct trust evaluations on the parent node at the same time in the detection of BlackHole Attack, the proposed method can prevent misjudgment. In addition, this study evaluates the behaviors through the ability of the interaction node and the expected behavior. Therefore, the proposed method will have less misjudgment.

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