Protecting Healthcare Systems Through an ML-Powered Cyberattack Detection Within Software-Defined Networking (SDN)

It is pointed out that the healthcare industry faces big problems when it comes to keeping private patient data safe in software-defined networks (SDNs). Healthcare apps need to have strong security measures because online risks are getting more complicated. This research work suggests a way to fix the problem by using machine learning techniques to find and stop a lot of different types of cyber risks in healthcare systems. Improving safety in healthcare apps is very important, and this project looks at how to do it. Protecting patient data and making sure healthcare networks work well are important for keeping patients healthy and for keeping people's trust in healthcare institutions. The project aims to make healthcare systems safer and more resilient by successfully fighting online dangers and improving network performance. In this project, ensemble methods like Stacking and Voting Classifiers were used to improve accuracy, and they were able to achieve 100% accuracy in finding cyberattacks on healthcare systems that used Software-Defined Networking. Built an easy-to-use Flask-based front end with a safe login that can be used in healthcare situations.