A Layered Intrusion Detection System for Critical Infrastructure Using Machine Learning

Security of critical infrastructures is very important and remote healthcare systems are of those critical infrastructures which need more attention regarding security issues. Remote healthcare systems collect data of patients continuously and react appropriately. Although personal medical data needs to be protected, security issues are ignored in most of the remote healthcare systems. Therefore, in this paper, our research goal is to propose an architecture that performs secure remote healthcare system. We aim to offer a secure framework for remote healthcare systems that preserve the data of the system as safe as possible against common network attacks including Denial of Service (DoS) and User to Root (U2R) attacks. To do so, we designed an intrusion detection system (IDS) using one of the machine learning algorithm, Support Vector Machine (SVM). After implementing our method, the evaluation parameters of the layered architecture of IDS prove the efficiency of our proposed framework.