Accurate threat hunting in industrial internet of things edge devices

作     者：Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Abbas Yazdinejad;Behrouz Zolfaghari;Ali Dehghantanha;Hadis Karimipour;Gautam Srivastava;Reza M.Parizi

作者机构：Cyber Science LabSchool of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software EngineeringUniversity of CalgaryAlbertaCanada Department of Mathematics and Computer ScienceBrandon UniversityBrandonCanada College of Computing and Software EngineeringKennesaw State UniversityGAUSA Research Center for Interneural ComputingChina Medical UniversityTaichungTaiwanChina Department of Computer Science and MathematicsLebanese American UniversityBeirut1102Lebanon 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2023年第9卷第5期

页      面：1123-1130页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods 

摘      要：Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.