A Data-Driven Approach to Cybersecurity: Advanced Analytics for Identifying and Preventing Security Breaches

Abstract

With the rise in cyber threats, traditional cybersecurity measures have become less effective in preventing sophisticated attacks. In this systematic review, we explore the potential of data-driven approaches—particularly machine learning (ML), deep learning (DL), and big data analytics—in identifying and preventing security breaches. By analyzing the latest studies, this paper evaluates the effectiveness of these technologies in improving threat detection and response times. We delve into the advantages of these approaches, including their ability to detect unknown attacks and scale in real-time environments, while also discussing the challenges associated with false positives, computational demands, and model interpretability. The review further investigates the integration of big data with machine learning to create more holistic, adaptive systems capable of tackling multi-dimensional cyber threats. Despite the promising outcomes, several barriers, such as model opacity and the need for robust real-time learning, remain. The research also underscores the growing importance of explainable AI (XAI) in fostering trust and transparency in cybersecurity applications. Overall, while data-driven approaches provide enhanced security, overcoming current limitations will require continued research into hybrid models, adaptive learning, and explainability to ensure a secure digital future.