Secure Deep Learning Techniques for Predictive Analytics in Industrial IoT Systems

Abstract

The rapid adoption of Industrial Internet of Things (IIoT) technologies has enabled large-scale deployment of predictive analytics for applications such as predictive maintenance, anomaly detection, and process optimization. Deep learning (DL) models have demonstrated superior capability in extracting complex temporal and spatial patterns from heterogeneous industrial sensor data. However, their deployment in IIoT environments introduces significant security and privacy challenges, including adversarial attacks, model poisoning, inference leakage, and exposure of sensitive operational data. These risks are amplified by distributed architectures, resource-constrained edge devices, and non-IID data distributions typical of industrial settings. This paper investigates secure deep learning techniques tailored for predictive analytics in IIoT systems and proposes an integrated security-aware framework that combines federated learning, differential privacy, secure aggregation, and adversarial training. The proposed methodology aims to preserve data confidentiality, ensure robustness against malicious clients and adversarial inputs, and maintain predictive performance under realistic industrial constraints. Mathematical formulations, algorithmic design, and pseudocode for the secure federated adversarial learning pipeline are presented to demonstrate practical implementation feasibility. Through analytical discussion and simulated experimental evaluation, the hybrid framework is shown to significantly reduce attack success rates and mitigate privacy leakage while maintaining competitive prediction accuracy. The findings highlight the importance of balancing robustness, privacy, and computational efficiency in industrial deployments. The paper concludes with implementation guidelines and future research directions for achieving resilient and privacy-preserving predictive intelligence in next-generation IIoT ecosystems.