Threat Mitigation in Big Data Ecosystems: Machine Learning-Driven Anomaly Detection for Zero Trust Network Security Architectures

Abstract

Big data ecosystems, characterized by massive volume, velocity, and variety of data, present complex and dynamic attack surfaces that traditional perimeter-based security models struggle to defend. The inherent complexity and distributed nature of these environments make them prime targets for sophisticated cyberattacks, including insider threats, data exfiltration, and advanced persistent threats (APTs). The Zero Trust Network Security Architecture (ZTNA) paradigm, operating on the principle of "never trust, always verify," offers a robust framework for securing such environments. However, effectively implementing Zero Trust mandates continuous, granular monitoring and real-time threat assessment across the entire data lifecycle, a challenge compounded by the scale of big data. This paper explores the critical integration of machine learning (ML)-driven anomaly detection as a cornerstone for threat mitigation within Zero Trust big data ecosystems. ML algorithms, trained on vast streams of operational telemetry, network flows, user behavior, and application logs, enable the identification of subtle, evolving deviations from established baselines that signify potential malicious activity. Techniques such as unsupervised learning (e.g., clustering, autoencoders) excel at detecting novel threats without predefined signatures, while supervised and semi-supervised methods enhance detection of known attack patterns and reduce false positives. Deep learning models, including recurrent neural networks (RNNs) and transformers, further improve accuracy by capturing complex temporal dependencies and contextual relationships within high-dimensional big data.