Intelligent Intrusion Detection System for Industrial Internet of Things Environment

作     者：R.Gopi R.Sheeba K.Anguraj T.Chelladurai Haya Mesfer Alshahrani Nadhem Nemri Tarek Lamoudan 

作者机构：Department of Computer Science and EngineeringDhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and EngineeringK.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication EngineeringSona College of TechnologySalem636005India Department of Electronics and Communication EngineeringPSNA College of Engineering and TechnologyDindigul624622India Department of Information SystemsCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information SystemsCollege of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of MathematicsCollege of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第2期

页      面：1567-1582页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 13[艺术学] 081104[工学-模式识别与智能系统] 08[工学] 0804[工学-仪器科学与技术] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP1/338/40) Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R237) Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia 

主　　题：Intrusion detection system artificial intelligence machine learning industry 4.0 internet of things 

摘      要：Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival *** classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and *** resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 *** CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection *** CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with ***,the OWKELM technique is applied for the intrusion detection and classification *** addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)*** utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better *** order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.