Predicting and mitigating cyber threats through data mining and machine learning

Abstract

With cyber threats evolving alongside technological progress, strengthening network resilience to combat security vulnerabilities is crucial. This research extends cyber-crime analysis with an innovative approach, utilizing data mining and machine learning to not only predict cyber incidents but also reinforce network robustness. We introduce a real-time data collection framework to provide up-to-date cyberattack data, addressing current research limitations. By analyzing collected attack data, we identified temporal correlations in attack volumes across consecutive time frames. Our predictive model, developed using advanced machine learning and deep learning techniques, forecasts the frequency of cyber-attacks within specific time windows, demonstrating over a 15% improvement in accuracy compared to conventional baseline models. The methodologies employed include the use of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) for capturing complex patterns in time series data, and the integration of a sliding window technique to transform raw data into a format suitable for supervised learning. Our experiments evaluated the performance of various models, including ARIMA, Random Forest, Support Vector Regression, and K-Nearest Neighbors Regression, across multiple scenarios. Furthermore, we developed a Power BI platform for visualizing global cyber-attack trends, providing valuable insights for enhancing cybersecurity defences. Our research demonstrates that cyber incidents are not entirely random, and advanced AI tools can significantly enhance cybersecurity defences by analyzing patterns and trends from previous instances. This comprehensive approach not only improves prediction accuracy but also offers a robust framework for reducing the risk and impact of future cyber-crimes through enhanced detection and prediction capabilities.