The Application of Machine and Deep Learning Models for Detecting Malware Threats in Healthcare Organizations

Today, information technologies are developing very quickly. They are mostly applied in all fields, including healthcare, where confidential information about patients is stored. However, the fast development of digitalization has intensified the appearance of new cyber threats. One of these kinds of threats carries a considerable risk to healthcare organizations. This threat is called malware. It covers various ways to steal or disrupt computers, installed systems, and software. While traditional protection approaches struggle to effectively counter malware, adopting and using machine learning and neural networks for data security against malware in healthcare emerges as the most useful approach. This study evaluates the effectiveness of various machine learning and deep learning algorithms, such as Random Forest, XGBoost, CatBoost, AdaBoost, Dense neural network, and Hybrid GRU-LSTM neural network in analyzing and classifying cybersecurity threats in healthcare. The utilized methodology describes the significant steps in dataset preprocessing, including data cleaning, normalization, feature selection, class balancing, and application of machine learning and neural network models for classification. The efficiency of all models was evaluated with four classification metrics: accuracy, precision, recall, and F1-score. All of them were successful, with the highest accuracy score of 0.989 for the Random Forest model. XGBoost and CatBoost had slightly lower scores of 0.982 and 0.974, respectively. DNN and GRU-LSTM reached accuracy scores of 0.960 and 0.966. The AdaBoost had the lowest accuracy score of 0.953. The experiments demonstrated the supremacy and robustness of the Random Forest model in this experiment and proved its applicability in many other tasks in the Artificial Intelligence field.