The essential role of intrusion detection is to manage the critical infrastructure to detect malicious activity competently concerning the Internet of Things (IoT). The IoT network is used to communicate and control information among various components composing a critical system. The essential inclination of infrastructure swerves to confront security issues and challenges as network systems are examined to expose cyber-related threats. Additionally, accessing real-time information from the cloud leads to a massive problem of latency. Fog computing is a novel archetype to encompass the cloud to the network edge with practical computation and critical infrastructure. The fog layer makes the device vulnerable to numerous attacks because of rapid access to resources. The practical way of addressing these issues is to detect intrusions effectively and trace the path leading to the attack source. The intent of this paper is to present a security mechanism and assure truthful operation of IoT networks with the intrusion detection system. A network intrusion detection system is proposed based on the conception of the Exact Greedy Boosting ensemble method for device implementation in the fog node because of protecting critical infrastructure from timely and accurate detection of malicious activities. The proposed model explores the traffic flow monitoring in novel IoT Intrusion Dataset 2020(IoTID20) network traffic by identifying and classifying the type of attack based on anomalies from normal behavior. Further, the paper estimates the complete experimentation performance and evaluations with competitive machine learning algorithms. The experimental observation of the simulation work is evident in the proposed model's efficiency and robustness in categorizing the attacks.

入侵检测的基本作用是管理关键基础设施，以检测与物联网 (IoT) 相关的恶意活动。物联网网络用于在构成关键系统的各个组件之间进行信息通信和控制。随着网络系统被检查以暴露与网络相关的威胁，基础设施的基本倾向转向应对安全问题和挑战。此外，从云端访问实时信息会导致严重的延迟问题。雾计算是一种新颖的原型，通过实用计算和关键基础设施将云涵盖到网络边缘。由于快速访问资源，雾层使设备容易受到多次攻击。解决这些问题的实际方法是有效地检测入侵并跟踪导致攻击源的路径。本文的目的是提出一种安全机制，并通过入侵检测系统确保物联网网络的真实运行。为了保护关键基础设施免受恶意活动的及时准确检测，基于精确贪婪提升集成方法的概念，提出了一种网络入侵检测系统，用于雾节点中的设备实现。所提出的模型通过基于正常行为的异常识别和分类攻击类型来探索新型物联网入侵数据集 2020（IoTID20）网络流量中的流量监控。更多，该论文使用具有竞争力的机器学习算法估计了完整的实验性能和评估。模拟工作的实验观察表明，所提出的模型在分类攻击方面的效率和鲁棒性是显而易见的。