Description

Cyber incidents are becoming increasingly common and sophisticated. Significant cyber incidents (SCIs) can have a major impact on organizations, both financially and reputationally. This makes it more to noise. So there is a need for effective methods to classify and predict SCIs. Traditional machine learning methods, such as support vector machines and decision trees, can be difficult to train and tune, and they may not be able to handle the complexity of SCI data. Random forest is a machine learning algorithm that can be used to classify and predict SCIs. Random forest is a ensemble learning algorithm that builds multiple and outliers than traditional machine learning algorithms. The results showed that random forest had the best performance in terms of accuracy, precision and recall.