Intrusion Detection in IIoT Leveraging Deep Transfer Learning

Abstract

The Industrial Internet of Things(IIoT)hasrevolutionizedindustrial automation and monitoring, connecting a multitude of heterogeneous devices to enhance productiv ity and operational efficiency. However, this interconnectivity has also exposed in dustrial networks to a wide spectrum of cybersecurity threats, including Distributed Denial-of-Service (DDoS) attacks, zero-day exploits, spoofing, and advanced persis tent threats. Traditional Machine Learning (ML) and Deep Learning (DL)-based In trusion Detection Systems (IDSs) are often insufficient in IIoT contexts due to chal lenges such as limited labeled datasets, severe class imbalances, high-dimensional traffic data, and the dynamic nature of zero-day attacks. Consequently, conventional IDSs struggle to achieve high detection accuracy while maintaining computational efficiency on resource-constrained IIoT devices. To address these limitations, this research proposes a robust Transfer Learning (TL)-based IDS framework that leverages pre-trained Convolutional Neural Net works (CNNs) and fine-tunes them on IIoT-specific traffic datasets. The framework incorporates ensemble learning strategies to combine the strengths of multiple classifiers, thereby improving resilience against diverse attack types and mitigating false positives. Additionally, hyperparameter optimization techniques are employed to enhance model performance and reduce computational overhead, enabling deployment on edge devices with limited processing capabilities. Extensive experiments are conducted on benchmark IIoT datasets to evaluate the effectiveness of the proposed system. The results demonstrate that the framework achieves superior detection accuracy, robust generalization to unseen attacks, and improved performance in scenarios with imbalanced data compared to traditional ML/DL methods. This study provides a practical, scalable, and efficient solution for safeguarding IIoT networks against evolving cyber threats, highlighting the poten tial of combining Transfer Learning, ensemble methods, and optimization for next generation intrusion detection systems.