A Review on Cyber Threats Detection in IoMT using AI Algorithms

The Internet of Medical Things (IoMT) has changed the landscape of healthcare from face to face interactions to real time monitoring, diagnosis and treatment. While the IoMT has progressed tremendously, it has also exposed the threats to its rapid growth, including ransomware, breaches of data and unauthorized access to internet-connected devices. The continued rapid growth of IoMT is compounded with resilient, connected, and outdated device security. Main issues of IoMT enabled devices are security and privacy due to limited control on transmitted data and reliance on wireless transmission for transfer of data making the network vulnerable to attack. Traditional methods, based on shallow learning, often face low recognition accuracy due to difficulties in handling large intrusion datasets and selecting the right features. This paper will explore machine learning, deep learning, hybrid based models and AI based techniques to assist professionals with detecting, and preventing threats in IoMT. Encryption can slow down data transmission, potentially affecting realtime monitoring and decision-making in critical health scenarios. It will focus on disruptive attacks, data breaches, vulnerabilities, and mitigation techniques. To protect the IoMT on a broad scale, it will explore associating advanced promising technologies such as homomorphic encryption, blockchain, and ensemble based intrusion detection systems. Additionally, the implications of regulatory support for privacy and security along with HIPAA and GDPR will also be explored. The study will propose a secure-encrypted network with systems, processes, data insight, interoperability, users, and a scalable framework within it to support cyberspace, and device threats surrounding the IoMT ecosystem. The main outcome of this research focuses on performance of the proposed cyber attack detection model and will be evaluated using key metrics like accuracy, false positive rate and false negative rate. Accurately detect cyber attacks in the Internet of Medical Things (IoMT) environment. Enhance data security and patient privacy through integrated Deep Learning and encryption methods.