AI-Augmented Big Data Analytics for Real-Time Cyber Attack Detection and Proactive Threat Mitigation

Sharmin Sultana; Mukther Uddin; Md Аsikur Rаhmаn Chy; Syed Nazmul Hasan; Emran Hossain; Harleen Kaur; Md Nazibullah Khan; Jobanpreet Kaur

doi:10.22399/ijcesen.3564

Authors

Sharmin Sultana School of Business, International American University, Los Angeles, California, USA https://orcid.org/0009-0005-7213-4504
Mukther Uddin School of Business, International American University, Los Angeles, California, USA https://orcid.org/0009-0009-0995-8761
Md Аsikur Rаhmаn Chy School of Business, International American University, Los Angeles, California, USA https://orcid.org/0009-0002-6550-7104
Syed Nazmul Hasan College of Technology & Engineering, Westcliff University, Irvine, CA 92614, USA https://orcid.org/0009-0008-0977-595X
Emran Hossain Department of Business Administration, Humphreys University, Stockton, California, USA https://orcid.org/0009-0005-2080-780X
Harleen Kaur College of Technology & Engineering, Westcliff University, Irvine, CA 92614, USA https://orcid.org/0009-0009-7062-0700
Md Nazibullah Khan School of Business, International American University, Los Angeles, California, USA https://orcid.org/0009-0008-4132-1413
Jobanpreet Kaur Westcliff University

DOI:

https://doi.org/10.22399/ijcesen.3564

Keywords:

Cyber security, Big Data Analytics, , Machine Learning, Anomaly Detection, Threat detection, Malware Detection, Real time monitoring, Predictive Analytics, AI-powered attacks

Abstract

Big data analytics, as used in defense, is the capacity to gather vast amounts of digital data for analysis, visualization, and decision-making that might aid in anticipating and preventing cyberattacks. When combined with security technologies, it improves it position in terms of cyber defense. They enable companies to identify behavioral patterns that point to network dangers. With its potent capabilities to tackle the increasing scope, variety, and complexity of cyberthreats, big data analytics has become a disruptive force in contemporary cybersecurity. Traditional data processing methods fall short in managing the massive volumes, varieties, and velocities (3Vs) characteristic of big data. This paper explores the foundational principles of big data analytics, including its core dimensions and key application areas such as healthcare, transportation, finance, education, and social media. The study further investigates the classification of cyberattacks malware, phishing, ransomware, and advanced persistent threats (APTs) and their evolving complexity due to AI-powered automation, IoT proliferation, and multi-vector intrusion techniques. It is highlighted how crucial big data is to supporting real-time threat detection, predictive modelling, and automated incident response. Techniques such as behavioral analysis, threat intelligence integration, and anomaly detection are examined for their effectiveness in identifying sophisticated attacks like polymorphic malware and zero-day exploits. Ultimately, this paper highlights how big data analytics enhances cybersecurity capabilities by delivering predictive, prescriptive, diagnostic, and cyber-specific insights that empower proactive threat mitigation and ensure digital resilience.

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AI-Augmented Big Data Analytics for Real-Time Cyber Attack Detection and Proactive Threat Mitigation

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