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Decision-Making Model for Securing IoT Devices in Smart Industries

机译：在智能行业中保护物联网设备的决策模型

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摘要

The industrial Internet-of-Things (IIoT) is a powerful Internet of Things (IoT) application that enables industrial growth by ensuring transparent communication among the various entities of a company such as the manufacturing locations, design hubs, and packaging units. However, current industrial architectures are unable to efficiently deal with advanced security issues that come with this communication due to the distributed and expandable nature of IIoT networks. Furthermore, from a security perspective, malicious devices with the objective of modifying data from within the premises of the network pose a high risk for the IIoT. Therefore, introducing intelligent decision-making models to the IIoT can enhance our ability to examine any collected data in a more structured, efficient, and secure manner. In this article, we provide a decision-making model for securing IIoT data. The proposed model, based on the Technique for Order Preference by Similarity to the Ideal Solution, can provide secure information transmission and recording/storage using various communicating parameters. The degree of trust of the IoT devices is analyzed using these parameters. Simple additive weighting is integrated into the proposed model to remove inefficient and ill-structured parameters. The proposed model is validated using various spectrum sensing and security parameters against a baseline method for the IIoT. Simulation results show that the proposed model is approximately 85% more efficient in identifying malicious nodes and denial-of-service threats compared to the baseline method.
机译：工业互联网（IOT）是一种强大的东西互联网（物联网）应用，通过确保制造地点，设计中心和包装单元等各种实体之间的透明通信来实现工业增长。然而，由于IIT网络的分布式和可扩展性，目前的工业架构无法有效地处理此沟通的高级安全问题。此外，从安全的角度来看，恶意设备具有从网络的房屋内修改数据的目标对IIOT构成了高风险。因此，向IIOR引入IoT的智能决策模型可以提高我们以更具结构性，高效和安全的方式检查任何收集的数据的能力。在本文中，我们提供了一种用于保护IIOT数据的决策模型。所提出的模型，基于通过与理想解决方案相似的顺序优先的技术，可以使用各种通信参数提供安全信息传输和记录/存储。使用这些参数分析物联网设备的信任程度。简单的添加剂加权集成到所提出的模型中以消除效率低下和结构性的参数。通过针对IIOT基线方法的各种频谱传感和安全参数验证所提出的模型。仿真结果表明，与基线方法相比，拟议模型在识别恶意节点和拒绝服务威胁方面的效率大约为85％。

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