Deploying AI-driven anomaly detection models for identifying advanced persistent threats in large-scale distributed network environments.
International Research Journal of Modernization in Engineering Technology and Science · 2025
Increasingly distributed enterprise networks and cloud-edge architectures have expanded the attack surface, enabling adversaries to conduct long-dwell, low-and-slow campaigns that evade signature-based defenses.This paper presents a comprehensive approach to deploying AI-driven anomaly detection for identifying advanced persistent threats (APTs) at scale.We outline a reference architecture that fuses high-volume telemetry flow records, endpoint events, identity logs, DNS, and cloud control-plane data into a streaming feature store, and apply a layered analytic stack combining unsupervised representation learning, graph-based detection, and behavior modeling.Specifically, autoencoders and contrastive self-supervision capture rare deviations in host and user time series; dynamic graph neural networks infer suspicious multi-hop relationships across assets; and sequence models characterize tactic-technique transitions in MITRE ATT&CK space.To meet data sovereignty and privacy constraints, we adopt federated and split-learning schemes with differentially private aggregation, while online drift monitors recalibrate models under shifting workloads.We propose operational metrics beyond ROC alert time-to-triage, precision at k, mean time to detect, footprint on endpoints, and analyst actionability and report findings from a multi-region deployment emulating tens of billions of events per day.Results show consistent uplift in early-stage lateral movement detection and credential-abuse discovery, with reduced false positives after feedback-informed thresholding.We discuss failure modes, including benign topology churn and red-team deception artifacts, and present guardrails for human-in-the-loop review, model governance, and incident response playbook integration.The work offers practical guidance for organizations seeking resilient, privacy-preserving APT detection across heterogeneous, distributed environments.We outline limitations label scarcity, explainability gaps, and compute costs and future work on causal inference, crosstenant federations, standardized telemetry schemas, and autonomous containment that respects operational safety constraints.Field trials across enterprises will validate durability against adaptation.