Design of Machine-Learning Framework for Cyber Attack Detection in Internet of Healthcare Things (IoHT): Critical Analysis

The IoHT has improved healthcare by exchanging patient data, remote monitoring, and service delivery. Since cyber threats have grown with technology, patient privacy and safety may be at stake. This study provides a machine-learning framework for IoHT ecosystem cyber threat identification, including its design and implementation. The IoHT cyber attack detection architecture protects patient data, medical equipment, and healthcare system confidence. The chapter recommends powerful AI, federated learning, and standardized security standards to detect IoHT cyber threats, which is tough. Health data transmitted between sensors and mobile devices could be stolen for identity theft or medical fraud. Attackers may also learn the victim’s health. This study addresses the issue that most IoHT devices lack computational power and cannot perform frequent cryptographic computations. High-processing IoHT systems reduce response time. Cryptographic methods must be lightweight, secure, and efficient for resource-constrained IoHT devices. The IoHT has improved healthcare by exchanging patient data, remote monitoring, and service delivery. Since cyber threats have grown with technology, patient privacy and safety may be at stake.