A SURVEY ON TRADITIONAL AND DEEP LEARNING TECHNIQUES FOR CYBER ATTACK DETECTION IN INTERNET OF MEDICAL THINGS

ABSTRACT

Apparently, connected devices on Internet has grown astronomically to about 18.8 billion globally in the year 2024. IoT is the network of physical devices equipped with sensors and applications to generate and transmit data over the internet. Internet of Medical Things (IoMT) is an emerging subset of IoT and it involves integration of health monitoring devices for the purpose of delivering quality health care and services in amore personalize and accessible way. The popularity of IoMT has led to making attackers with malicious intent to consistently launch attacks against IoMT infrastructure for their various selfish reasons. Recently, machine learning (ML) and deep learning (DL) have evolved and adopted for classification of attacks with a view for having effective intelligent schemes in place to identify various network-based attacks. This study surveyed some recent and relevant articles that applied ML and DL for prompt identification and mitigation of attacks in IoMT. This review provides more insights and understanding the benefit of adopting of ML and DL in IoMT landscape. The inclusion criteria used for the selection include: studies between 2018 and 2025 that were written using English Language only as well as the ones that applied ML and DL for the classification of attacks in IoMT. In conclusion, this work can provide further actionable insights to other researchers working in this domain based on the arguments presented in the surveyed papers.

Keywords: Cyber Security, Network Attacks, IoMT, Machine Learning, Smart Medical Devices