Multi-Modal Data Fusion Techniques for Improved Cybersecurity Threat Detection and Prediction

Abstract

In the era of increasing cyber threats, traditional threat detection methods often fall short in identifying and mitigating sophisticated attacks. This paper explores the concept of multi-modal data fusion techniques, which integrate various data sources to enhance cybersecurity threat detection and prediction. By combining data from network traffic, user behavior, system logs, and external threat intelligence, we aim to develop more robust and accurate predictive models. This paper reviews current methodologies, highlights challenges, and presents case studies demonstrating the effectiveness of multi-modal approaches in real-world cybersecurity scenarios.