Trust Scoring for Edge–Fog–Cloud IIoT Networks Using Deep Learning

Trust Management Systems (TMSs) have recently emerged as a behavioural complement to identity-based approaches in Industrial IoT (IIoT) cybersecurity by evaluating node trustworthiness. Deep Learning (DL)-based TMSs offer favourable detection over heuristic and Machine Learning (ML) models. The computational density of DL models introduces a trade-off between inference fidelity and deployment feasibility, particularly in Edge-Fog-Cloud (EFC) IIoT architectures where latency and resources are constrained. This work proposes an EFC architectural framework that relocates DL inference to the Fog layer, reducing Cloud communication latency and Edge resource exhaustion. A lightweight Long Short-Term Memory (LSTM)-based model derives continuous trust scores from header-derived, flow-aggregated features, with inference latency bounded through fixed-size sliding windows and stateless execution. The system is trained and evaluated on CIC-IoT-2023 across Denial-of-Service (DoS), Distributed DoS (DDoS), Mirai, and benign scenarios. System scalability is assessed through ns-3 network simulation under benign conditions, with full-system behaviour further evaluated under benign, DoS, and Mirai scenarios. Offline evaluation achieves F1-score 0.9996, accuracy 0.9997, ROC-AUC 0.9999, and PR-AUC 0.9997. Architectural evaluation yields a mean inference latency of 0.049 ms, a maximum enforcement latency of 0.120 ms, and a 302 kB deployment footprint. System simulation confirms a benign False Positive Rate (FPR) 0.07% and a maximum detection latency of 0.22 ms. DoS achieves recall 0.99999 and FPR 0.00186, and Mirai achieves recall 0.99997 with FPR 0. This demonstrates that DL-based trust inference is achievable on resource-constrained Fog nodes, establishing the work as a viable solution for trust evaluation in EFC IIoT deployments.