Advancing Network Resilience Through Data Mining and Machine Learning in Cybersecurity

The advancement of internet technology and growing involvement in the cyber world have made us prone to cyber-attacks inducing severe damage to individuals and organizations, including financial loss, identity theft, and reputational damage. With cyber threats evolving alongside technological progress, strengthening network resilience to combat security vulnerabil-ities is crucial. This research extends cyber-crime analysis with an innovative approach, utilizing data mining to not only predict cyber incidents but to reinforce network robustness. Although there are many strategies for intrusion detection, predicting upcoming cyber threats remains an open research challenge. Hence, this research seeks to utilize temporal correlations among attack frequencies within specific time periods to predict the future severity of cyber incidents. The research aims to address the current research limitations by introducing a real-time data collection framework that will provide up-to-date cyber-attack data. A correlation was identified in the reported attack volume across consecutive time frames through collected attack data analysis. This research introduces a predictive model that forecasts the frequency of cyber-attacks within a specified time window, using a historical record of attack counts. The research includes various machine learning and deep learning methods to develop a prediction system based on multiple time frames with an over 15% improvement in accuracy compared to the conventional baseline model. Namely, our research demonstrates that cyber incidents are not entirely random, and by analyzing patterns and trends in past incidents, developed AI techniques can be used to improve cybersecurity measures and prevent future attacks.