Generative Ensemble Learning for Robust Anomaly Detection in IIoT Financial Monitoring

Abstract

The widespread growth of Industrial Internet of Things (IIoT) systems reveals new vulnerabilities in financial data streams, especially in fraud and pattern anomalies detection mechanisms. Traditional detection methods used to detect anomalies have limited generalization abilities and consistently tend to produce unsatisfactory false-positive rates; the limitation of these two features is paramount in a large scale financial environment. This paper introduces a novelty in the form of a Generative Ensemble-learning Framework employing three distinct Generative Adversarial Network architectures—Deep Convolutional GAN, Wasserstein GAN and Conditional GAN—to create an ensemble with improved robustness for anomaly detection in IIoT-financial monitoring systems. By adorning the complements in magnifying the strengths of each GAN, ensemble-architecture represents the stark contrast from overfitting and reduction in detection variance to success, allowing the triumph of both the scientific progress and social welfare in essence. Experimental evaluation on benchmark IIoT financial data showed that our proposed framework produced much more significant improvements on false-positive rates (FPRs), F1-scores, and Area Under the Curve (AUC) compared to single GAN models. We further galvanized the interpretability and calibration with a weighted decision fusion strategy. This proposed ensemble method is capable of learning relatively quickly on streaming data in an adaptable manner and so functioning as a scalable foundational layer for deployment in real applications of automated financial monitoring. This study uniquely contributes with the pioneers' unconventional application of adversarial learning through Ensemble Learning and echoes the echo within the wider literature in securing IloT's financial institutions from data-driven Bonekennels.