A Robust Security Detection Strategy for Next Generation IoT Networks  

作　　者：Hafida Assmi Azidine Guezzaz Said Benkirane Mourade Azrour Said Jabbour Nisreen Innab Abdulatif Alabdulatif 

机构地区：[1]Technology Higher School Essaouira,Cadi Ayyad University,Essaouira,44000,Morocco [2]IMIA Laboratory,MSIA Team,Faculty of Sciences and Techniques,Moulay Ismail University of Meknes,Errachidia,50050,Morocco [3]CRIL-CNRS,Artois University,Lens,62300,France [4]Department of Computer Science and Information Systems,College of Applied Sciences,AlMaarefa University,Diriyah,Riyadh,13713,Saudi Arabia [5]Department of Computer Science,College of Computer,Qassim University,Buraydah,52571,Saudi Arabia

出　　处：《Computers, Materials & Continua》2025年第1期443-466,共24页计算机、材料和连续体（英文）

摘　　要：Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.

关 键 词：IoT security intrusion detection RF KNN SVM EL NSL-KDD Edge-IIoT 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]