Intrusion Detection with Machine Learning and Deep Learning Methods in IoT Healthcare

With the rapid evolution of microelectromechanical technologies, the Internet of Things (IoT) has dramatically transformed the healthcare landscape, leading to better patient care, greater operational efficiency, and improved health outcomes. IoT devices, including wearable sensors, remote monitoring systems, and intelligent medical equipment, have made it possible to provide continuous and personalized healthcare services. These devices can gather real-time information on patients, including vital signs, medication compliance, and activity patterns, enabling healthcare professionals to remotely oversee and manage patient conditions more efficiently. However, as the proliferation of interconnected devices expands, so does the risk of security breaches and system vulnerabilities. Devices in the IoT ecosystem naturally gather and send vast quantities of sensitive information, such as personal data and essential infrastructure controls. In the absence of robust security protocols, this data becomes an attractive target for cybercriminals, posing serious threats to individuals, healthcare institutions, and society at large. This paper focuses on detecting intrusion attacks to mitigate vulnerabilities within IoT healthcare systems, thereby protecting sensitive hospital data and ensuring patient safety. The experimental findings highlight the significant role of machine learning in improving IoT security. By enabling sophisticated anomaly detection, predictive threat analysis, and adaptive response strategies, machine learning effectively secures interconnected devices and networks against potential cyber threats.