Description

This study focuses on the importance of Cyber Security and the impact of COVID-19 on cyber security. Data Mining and Machine Learning techniques are used to predict, prevent, and detect Significant Cyber Incidents (SCI). The dataset is divided into pre-pandemic and post-pandemic SCI, and well-known ML classifiers are used for classification. The results show that SVM and RF are better classifiers than others, and Asia is predicted to be the most affected continent by SCI. This study highlights the importance of Cyber Security in protecting internet systems from Significant Cyber Incidents (SCI) and the role of Data Mining and Machine Learning (DM-ML) in prediction, prevention, and detection of SCI. The study uses a dataset of pre-pandemic and post-pandemic SCI, divided into two subsets, and applies DM techniques for feature extraction and well-known ML classifiers such as Naïve Bayes, Support Vector Machine, Logistic Regression, and Random Forest for classification. A centralized classifier approach is used to maintain a single centralized dataset from inputs across six continents. The results show that SVM and RF are better classifiers than others, and Asia is predicted to be the most affected continent by SCI. The study concludes with better accuracy in predicting which type of SCI can occur in which part of the world. Finally, the impact of COVID-19 on cyber security is also discussed.