Leveraging Big Data Analytics for Enhanced Cybersecurity: A Comprehensive Analysis of Threat Detection, Incident Response, and SIEM Systems
Ankur Partap Kotwal · International Journal of Scientific Research in Computer Science Engineering and Information Technology · 2024
This article comprehensively analyzes big data analytics applications in cybersecurity, focusing on threat detection, incident response, and Security Information and Event Management (SIEM) systems. The article explores how organizations leverage big data analytics to enhance their cybersecurity posture through advanced threat detection mechanisms, improved incident response capabilities, and sophisticated SIEM implementations. The article examines various aspects of modern cybersecurity systems, including anomaly detection, predictive analytics, real-time monitoring architectures, and root cause analysis frameworks. Through detailed case studies of major platforms, including Google Security Analytics, IBM QRadar, and Splunk, the article provides insights into practical implementations and their impact on organizational security. The article also addresses emerging technologies such as quantum computing and their implications for future cybersecurity frameworks. By analyzing implementation guidelines, best practices, and research opportunities, this article offers valuable insights for organizations seeking to enhance their cybersecurity capabilities through big data analytics while providing a framework for future developments in this rapidly evolving field.