Securing Healthcare Systems: Modern Threats and Strategic Developments in Cybersecurity

In the present time where societies heavily depend on the use of information technology in healthcare organizations, the possibility of cyber threats put at risk patients’ information and even the healthcare organization’s services. This paper focuses on the use of complex triangular algorithms in the protection of healthcare systems. This research work has deployed and tested four systems, namely anomaly detection, AES encryption, machine learning IDS, and blockchain. The detection rate of the given anomaly detection algorithm for the above-mentioned factors has been 95% to specify the significant deviation from normal behavior. AES encryption of the healthcare data has been performed with very low overhead consuming only 50–55 ms for encryption and 45–50 ms for decryption. A machine learning case on Random Forest classifier for intrusion detection showed 97% accuracy further improving the true and false positives of negatives. The data has been thoroughly protected with a transaction time of 150 ms proving the competence of blockchain as a highly secure system. These discoveries provoke the necessity of a multilevel security approach, where several of the most modern technologies are used to reinforce the wall against cyber threats. Consequently, it does not just improve the performance of detection and response to threats but also maintains the ability of healthcare systems to evolve and counter the threats effectively.