Enhancing Cybersecurity through Advanced Threat Detection: A Deep Learning Approach with CNN for Predictive Analysis of AI-Driven Cybersecurity Data

Abstract

In the dynamic realm of cybersecurity, the increasing complexity of cyber threats necessitates innovative and resilient solutions. This study addresses the critical requirement for heightened threat detection by presenting an advanced deep learning strategy employing Convolutional Neural Networks (CNN). Utilizing the capabilities of artificial intelligence (AI), our model seeks to anticipate cybersecurity threats through the predictive analysis of intricate cybersecurity data. Nonetheless, existing challenges in the field revolve around the limitations of conventional methods in precisely identifying intricate threats and adapting to evolving attack methodologies. To surmount these challenges, we introduce a groundbreaking CNN-based model that exploits the hierarchical feature learning attributes of convolutional networks, facilitating more efficient identification of patterns and anomalies in cybersecurity data. The proposed model is crafted to propel the cybersecurity domain forward, offering a proactive and adaptable defense mechanism against emerging threats, thereby reinforcing the resilience of digital systems amidst a continually expanding threat landscape.