Identification of Cyberattacks in IoT-Powered Healthcare

The rapid advancement in healthcare technology has brought significant benefits but also introduced new vulnerabilities. As healthcare organizations adopt digital technologies, they face growing risks to the security of medical processes and patient safety. This research explores the current state of cybersecurity in the healthcare sector, highlighting the threats these organizations face. In later times, cyberattacks on the Web of Wellbeing Things (IoHT) have ceaselessly been developing, and so it is vital to create strong countermeasures. In any case, there is a need of freely accessible datasets reflecting cyberattacks on IoHT, primarily due to security concerns. This paper exhibits the business of a dataset, ECU-IoHT, which builds upon an IoHT environment having distinctive assaults performed that abuse different vulnerabilities. The ECU-IoHT dataset, made in 2021, comprises of 9 highlights and 4 sorts of assaults, with a add up to of 111,207 records. It is partitioned into a preparing set of 88,965 records and a testing set of 22,242 records. This dataset was planned to assist the healthcare security community in analysing assault behaviour and creating vigorous countermeasures. Utilizing the ECU-IoHT dataset, this research investigate applies progressed calculations, counting RNN and CNN, to analyse and anticipate potential vulnerabilities in healthcare frameworks. The utilize of RNN and CNN models improves the prescient capabilities of cybersecurity systems by precisely distinguishing designs and inconsistencies that may flag security breaches, illustrating the basic require for coordination. AI-driven courses of action to protect fragile well-being data and ensure the movement of remedial organizations. The models illustrated tall execution, with CNN accomplishing an exactness of 98.9% for parallel classification and RNN accomplishing 98.9% for multi-class classification, both appearing solid exactness, review, and F1 scores.