A Secure Deep Learning Framework for Predictive Analytics in Industrial IoT

Abstract

The Industrial Internet of Things (IIoT) is transforming the industry by allowing smart connectivity among machines, sensors, and enterprise systems. The amount of information generated in an industrial environment is so vast that it requires predictive analytics to maximise efficiency, reduce downtime, and simplify the process. Due to its capability to extract high-level features of complex, heterogeneous data sets, deep learning (DL) has become an influential predictive analytics tool in IIoT. However, the growing susceptibility of IIoT infrastructures to cyberattacks and data breaches, not to mention adversarial manipulation of DL models, poses a threat to security. The paper presents and discusses a secure deep learning architecture to support predictive analytics in the Industrial IoT. The framework combines the concept of multi-layered DL architectures and security modules to ensure the confidentiality of data, model integrity, and reliability of the decisions. We can apply it to predictive maintenance, anomaly detection, demand forecasting, and quality control in real time. We examine how federated learning, homomorphic encryption, and adversarial training are techniques that can improve the resilience of DL frameworks to cyber threats. The research identifies the rationale behind the adoption of secure DL in IIoT with a focus on the trade-off between predictive quality and security demands. Findings of the latest studies suggest secure DL models have the potential to attain more than 90 per cent predictive accuracy and reduce the risk of poisoning and evasion attacks. However, we identify the following weaknesses: computational load, asymmetry of data, and unintelligibility of models, which can significantly challenge their implementation. The paper concludes that safe DL systems are a potential avenue for making trustworthy predictive analytics in IIoT possible. The way forward in future work includes lightweight secure DL models on the edge, explainable AI for transparency, and industry-wide collaborative security.