Machine Learning Based Cybersecurity Framework for Healthcare Application

The medical field has recently started to depend more on IoT devices to ensure priority protection of patient data and integrity of medical systems. In this context, the project proposes an healthcare cybersecurity solution that would leverage machine learning in order to be able to detect and manage cyberattacks. Machine learning and AI algorithms like Kitsune that recognize network anomalies to detect the most secure risk by alerting potential newcomers probably employ the project funded in Machine learning and AI due to its ability to create projects In this context, Docker containers create an IoT network for the effective traffic and other way to build feature detection and anomaly detection algorithms. With its combined expertise, the system utilizes hping3 and tcpdump to create a meaningful process of inspection combined with an anomaly detection mechanism.