Anomaly Detection Framework in Fog-to-Things Communication for Industrial Internet of Things

作     者：Tahani Alatawi Ahamed Aljuhani 

作者机构：College of Computing and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第10期

页      面：1067-1086页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Anomaly detection anomaly-based IDS fog computing Internet of Things(IoT) Industrial Internet of Things(IIoT) IDS Industrial Control Systems(ICSs) 

摘      要：The rapid development of the Internet of Things(IoT)in the industrial domain has led to the new term the Industrial Internet of Things(IIoT).The IIoT includes several devices,applications,and services that connect the physical and virtual space in order to provide smart,cost-effective,and scalable *** the IIoT has been deployed and integrated into a wide range of industrial control systems,preserving security and privacy of such a technology remains a big *** anomaly-based Intrusion Detection System(IDS)can be an effective security solution for maintaining the confidentiality,integrity,and availability of data transmitted in IIoT *** this paper,we propose an intelligent anomalybased IDS framework in the context of fog-to-things communications to decentralize the cloud-based security solution into a distributed architecture(fog nodes)near the edge of the data *** anomaly detection system utilizes minimum redundancy maximum relevance and principal component analysis as the featured engineering methods to select the most important features,reduce the data dimensionality,and improve detection *** the classification stage,anomaly-based ensemble learning techniques such as bagging,LPBoost,RUSBoost,and Adaboost models are implemented to determine whether a given flow of traffic is normal or *** validate the effectiveness and robustness of our proposed model,we evaluate our anomaly detection approach on a new driven IIoT dataset called XIIoTID,which includes new IIoT protocols,various cyberattack scenarios,and different attack *** experimental results demonstrated that our proposed anomaly detection method achieved a higher accuracy rate of 99.91%and a reduced false alarm rate of 0.1%compared to other recently proposed techniques.