CLASSIFICATION OF CYBER ATTACKS IN INTERNET OF MEDICAL THINGS USING PARTICLE SWARM OPTIMIZATION WITH SUPPORT VECTOR MACHINE

Abstract

The Internet of Medical Things (IoMT) is a tangible application of the IoT inside the healthcare sector, presenting an opportunity to enhance the existing healthcare system and enhance patient well-being and quality of life. The presence of vulnerabilities in the ongoing transmission of IoMT data is a potential risk, since it enables unauthorised individuals to criminally get access to sensitive health information. This compromised data might then be used for malicious purposes. A multitude of security approaches have been suggested and developed in order to effectively mitigate the security problems associated with the IoMT. This paper aims to frame cyber-attack prediction as a classification issue, with a focus on the field of networking. The objective is to predict the specific kind of network attacks using machine learning algorithms. The classification of different forms of Denial of Service (DOS) assaults includes the following: BENIGN, MSSQL, DNS, LDAP, TFTP, SSDP, SYN Flood, PORTMAP, SNMP, NetBIOS, UDP, UDP-Lag, and WebDDoS. The findings indicate that the suggested PSO with SVM algorithm has a 97% accuracy rate, outperforming than existing algorithms.