Machine Learning-Based Methodology for Preventing Ransomware Attacks on Healthcare Sector

There is a big concern about security in health care organizations, to protect the important documents of patients, doctors, staff of the organizations and many more other information. In this current scenario where attacks have become common, proposing a new technology has become crucial concern. As Machine Learning is emerging in providing smart solutions in numerous applications and these techniques are also beneficial for network administrators to protect network infrastructure from multiple cyber-attacks at early stage. This research paper proposes a model to shield the hospital web server from directly receiving malicious packets. A dedicated machine learning trained server is suggested to be utilized in the health care network so that only authenticated data packet is transmitted inside the hospital network. Here in the paper Machine Learning approaches are used to identify malware, and among these techniques Random Forest proves to be the best algorithm for the early prediction of ransomware attacks. The dataset for training and testing machine learning model is taken from Canadian institute for Cybersecurity where data is pre-processed so, different validation code has been introduced like k-fold validation, confusion matrix, and Receiver Operating Characteristic Area Under the Curve to get a more rectify comparative analysis.