Classification and Prediction of Significant Cyber Incidents (SCI) Using Data Mining and Machine Learning (DM-ML)

Abstract

The rapid growth in technology and several IoT devices make cyberspace unsecure and eventually lead to Significant Cyber Incidents (SCI). Cyber Security is a technique that protects systems over the internet from SCI. Data Mining and Machine Learning (DM-ML) play an important role in Cyber Security in the prediction, prevention, and detection of SCI. This study sheds light on the importance of Cyber Security as well as the impact of COVID-19 on cyber security. The dataset (SCI as per the report of the Center for Strategic and International Studies (CSIS)) is divided into two subsets (pre-pandemic SCI and post-pandemic SCI). Data Mining (DM) techniques are used for feature extraction and well know ML classifiers such as Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) for classification. A centralized classifier approach is used to maintain a single centralized dataset by taking inputs from six continents of the world. The results of the pre-pandemic and post-pandemic datasets are compared and finally conclude this paper with better accuracy and the prediction of which type of SCI can occur in which part of the world. It is concluded that SVM and RF are much better classifiers than others and Asia is predicted to be the most affected continent by SCI.