CYBER THREAT HUNTING IN IOMT; ENHANCING SECURITY THROUGH PROACTIVE DETECTION

Abstract

The Internet of medical things (IoMT) is far too common in the medical field, in the exchange of medical information among the interconnected devices.this can be convenient in continuous check on the patients, it is also therat to security. This paper will highlight the number of cyber threats encountered through the utilization of the traditional security systems, which reacts once an attack has occurred.This paper shall focus on the early identification of cyber threats in the IoMT networks using a machine learning-based approach. The suggested system monitors the traffic and trains the network in which the IoMT devices will interact with each other. A possible indicator of an barred act is the abnormal or unpredictable presence in the stream of the data. This will facilitate in detecting both the known and unknown attacks before they can create severe destuction upon the network.The system will be in a position to verify the activity going on the internet and isolate the normal and potential activity providing quick response as well as enhanced security awareness. The primary idea of this work is how the safety and reliability of the IoMT-based healthcare systems can be enhanced through the suggestion of the proactive approach to their detection.