Big Data Analytics in Cybersecurity: Improving Threat Detection and Prevention

Authors

Manoj Kumar

Abstract

The increasing complexity and frequency of cyber threats have made it necessary for organizations and governments to move away from intuition-based decision-making in cybersecurity management for data-driven approaches. Big Data analytics tools will enable these organizations and companies to monitor intelligently the inflow of data through their secure channels, thus offering higher threat detection and prevention capabilities, optimizing response times, and offering greater overall security postures. This, in turn, will open the way for real-time processing of massive volumes of data for predictive insights, anomaly detection, and vulnerability identification long before actual exploitation may occur. Advanced analytics, machine learning, and AI are at the heart of cybersecurity frameworks that will help organizations outsmart threats with increased accuracy and speed. It describes the transformative power of Big Data analytics in cybersecurity, underlined by various case studies of companies and government agencies that have already integrated these technologies into their security operations. Further, it presents challenges and opportunities created by this data-driven paradigm shift, including privacy concerns, data governance, and skilled human resources required to manage such systems.

Keywords

Big Data Analytics, cybersecurity, threat detection, prevention, data-driven decisions, predictive insights, machine learning, anomaly detection, security posture, artificial intelligence, data governance

Big Data Analytics in Cybersecurity: Improving Threat Detection and Prevention. Manoj Kumar. 2020. IJIRCT, Volume 6, Issue 4. Pages 1-10. https://www.ijirct.org/viewPaper.php?paperId=2411106