An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

作     者：R Arthi S Krishnaveni Sherali Zeadally R Arthi;S Krishnaveni;Sherali Zeadally

作者机构：Computational IntelligenceSchool of ComputingSRMISTKattankulathurChennaiIndia College of Communication and InformationUniversity of KentuckyUSA 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2024年第21卷第10期

页      面：267-287页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

主　　题：deep neural network healthcare intrusion detection system IoT machine learning software-defined networks 

摘      要：The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every ***,it is essential to make medical services connected to internet,available in every remote location during these ***,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical ***,services in the healthcare ecosystem need rapid,uninterrupted,and secure *** solution provided in this research addresses security concerns and services availability for patients with critical health in remote *** research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare *** propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare *** addition,this system can efficiently monitor connected devices and suspicious ***,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application *** experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.