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<p>In recent decades, the increasing predominance of Internet of Things (IoT) intelligence on the market has driven the achievement of the IoT sector. IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is a critical unit to support data transmission among multiple nodes. However, the RPL is highly susceptible to several resource-based attacks since the nodes in the IoT-LLN have restricted battery, memory, and manipulation capabilities. Existing countermeasures often exhaust the energy of legitimate nodes, disrupt routing consistency, and incur computational overhead. These challenges motivate the development of a novel Lightweight Zero Trust-based Attack Detection (LZTAD) model for mitigating resource-based attacks in the IoT-LLN. Primarily, all nodes in the network are validated with the aid of the lightweight authentication process. The link quality is then evaluated to assess the routing performance of the RPL. Based on the routing behaviour, the proposed LZTAD scheme detects malicious activities rapidly in the network. Afterward, a quantized deep learning model has been executed to classify the different attacks, where the quantization technique is applied to optimize the model size. This lightweight approach expedites the proposed LZTAD model to achieve a superior detection rate of 97.3% in a scalable IoT-LLN environment.</p>

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A Lightweight Zero Trust Security Model for Mitigating Resource-based RPL Attacks in Scalable IoT-LLN Environments

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • K. K. Devi Sowndarya,
  • N. Rajganesh

摘要

In recent decades, the increasing predominance of Internet of Things (IoT) intelligence on the market has driven the achievement of the IoT sector. IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is a critical unit to support data transmission among multiple nodes. However, the RPL is highly susceptible to several resource-based attacks since the nodes in the IoT-LLN have restricted battery, memory, and manipulation capabilities. Existing countermeasures often exhaust the energy of legitimate nodes, disrupt routing consistency, and incur computational overhead. These challenges motivate the development of a novel Lightweight Zero Trust-based Attack Detection (LZTAD) model for mitigating resource-based attacks in the IoT-LLN. Primarily, all nodes in the network are validated with the aid of the lightweight authentication process. The link quality is then evaluated to assess the routing performance of the RPL. Based on the routing behaviour, the proposed LZTAD scheme detects malicious activities rapidly in the network. Afterward, a quantized deep learning model has been executed to classify the different attacks, where the quantization technique is applied to optimize the model size. This lightweight approach expedites the proposed LZTAD model to achieve a superior detection rate of 97.3% in a scalable IoT-LLN environment.