Big Data Analytics in Cybersecurity: Uncovering Threats, Vulnerabilities, and Attack Patterns for Prevention

Abstract

In recent years, Big Data Analytics (BDA) has emerged as a transformative force in the field of cybersecurity, offering novel approaches for threat detection, vulnerability management, attack pattern analysis, and predictive analytics. This systematic review examines the current state of BDA in cybersecurity, synthesizing findings from 45 selected studies. The review highlights the critical role of machine learning algorithms and real-time data processing in enhancing cybersecurity systems, enabling more accurate and timely identification of cyber threats. However, the integration of BDA is not without challenges, including issues related to data quality, scalability, integration complexity, and ethical concerns surrounding privacy and data governance. The review also discusses the role of predictive analytics in proactive threat mitigation and emphasizes the need for continuous improvements in Big Data systems. Furthermore, it explores the ethical implications of using personal data in cybersecurity and the importance of adhering to data protection regulations. This paper concludes by suggesting future directions for research, including the development of adaptive models, improvements in scalability, and the establishment of ethical frameworks to guide the responsible use of Big Data in cybersecurity.