The Internet of Things (IoT) plays a vital role in many application domains like battlefield surveillance, wildlife monitoring, disaster response, medical care, transportation, industry, smart home, smart cities, etc. However, this network is susceptible to various types of attacks due to its special features like sensing, intelligence, large scale, self-configuring, connectivity, heterogeneity, open and dynamic environment. It is significant to ensure security in the IoT network. In the scalable and dynamic IoT environment, conventional security mechanisms such as cryptography techniques, key management, intrusion detection system, anomaly detection, etc cannot be applicable, because it consumes more energy. Therefore, the IoT network requires a lightweight security mechanism for reliable and secure data transmission. A trust-based security solution solves many security-related problems. The proposed RFTrust model provides a trust-based lightweight solution for ensuring security in the IoT network. It is primarily designed to address the sinkhole attack in Routing Protocol for Low power and Lossy networks (RPL) based IoT environments. It enhances the trusted routing in the IoT environment by finding and removing sinkhole nodes in the network. The proposed model uses Random Forest (RF) and Subjective Logic (SL) to improve the network performance by identifying sinkhole attack. The mathematical analysis shows the applicability of the proposed model. The merits of the proposed work are highlighted by comparing performance with the existing similar protocols in terms of delivery ratio, throughput, average delay, energy consumption, false-positive rate, false-negative rate, and detection accuracy.

物联网 (IoT) 在战场监视、野生动物监测、灾害响应、医疗、交通、工业、智能家居、智慧城市等许多应用领域发挥着至关重要的作用。由于其具有传感、智能、大规模、自配置、连接性、异构性、开放和动态环境等特殊特性，因此受到攻击。确保物联网网络的安全性非常重要。在可扩展和动态的物联网环境中，传统的安全机制如密码技术、密钥管理、入侵检测系统、异常检测等无法适用，因为它消耗更多的能量。因此，物联网网络需要轻量级的安全机制来实现可靠、安全的数据传输。基于信任的安全解决方案解决了许多与安全相关的问题。提议的 RFTrust 模型提供了一种基于信任的轻量级解决方案，用于确保物联网网络的安全性。它主要用于解决基于低功耗和有损网络 (RPL) 的物联网环境的路由协议中的陷坑攻击。它通过查找和删除网络中的陷坑节点来增强 IoT 环境中的可信路由。所提出的模型使用随机森林（RF）和主观逻辑（SL）通过识别天坑攻击来提高网络性能。数学分析表明了所提出模型的适用性。通过将性能与现有类似协议在交付率、吞吐量、平均延迟、能耗、误报率、