Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

作     者：Okba Taouali Sawcen Bacha Khaoula Ben Abdellafou Ahamed Aljuhani Kamel Zidi Rehab Alanazi Mohamed Faouzi Harkat 

作者机构：Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia National Engineering School of MonastirUniversity of MonastirMonastir5000Tunisia Applied CollegeUniversity of TabukTabuk71491Saudi Arabia Department of ElectronicsFaculty of Engineering AnnabaAnnaba23000Algeria 

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47卷第11期

页      面：1593-1609页

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）] 

基　　金：supported by the Deanship of Scientific Research at the University of Tabuk through Research No.S-1443-0111 

主　　题：Machine learning data-driven technique KPCA KPLS intrusion detection IoT Internet of Medical Things(IoMT) 

摘      要：Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant ***,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning ***,designing and developing a lightweight detection mechanism is *** address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT *** proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and *** the pre-processing phase,data cleaning and normalization are *** the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection ***,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is *** check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved *** proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.