Leveraging AI and Machine Learning for Cybersecurity Threat Detection in Big Data Environments

Paper ID: EIJTEM_2026_13_1_103-110

Author's Name: Vijay Kiran Katikala

Volume: 13

Issue: 1

Year: 2026

Page No: 103-110

Abstract:

The rapid expansion of digital infrastructures has led to an unprecedented rise in cyber threats, making traditional security mechanisms insufficient to counter evolving attack vectors. This paper explores how Artificial Intelligence (AI) and Machine Learning (ML) can enhance cybersecurity by detecting, predicting, and mitigating threats in Big Data environments. AI-driven models, including deep learning, anomaly detection, and behavior-based analysis, can process massive datasets in real time to identify malicious activities with higher accuracy than rule-based systems. By leveraging predictive analytics, threat intelligence, and automated response mechanisms, AI-powered cybersecurity solutions significantly reduce false positives and enable proactive defense strategies. This study also highlights challenges such as adversarial attacks, data privacy concerns, and computational overhead, offering insights into future research directions for securing digital ecosystems against sophisticated cyber threats.

Keywords: Artificial Intelligence, Machine Learning, Cybersecurity Threat Detection, Big Data Security, Anomaly Detection.

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