An Adaptive Trust Boundary Protection for IIoT Networks Using Deep-Learning Feature-Extraction-Based Semisupervised Model

The rapid development of Internet of Things (IoT) platforms provides the industrial domain with many critical solutions, such as joint venture virtual production systems. However, the extensive interconnection of industrial systems with corporate systems in industrial Internet of Things (IIoT) networks exposes the industrial domain to severe cyber risks. Because of many proprietary multilevel protocols, limited upgrade opportunities, heterogeneous communication infrastructures, and a very large trust boundary, conventional IT security fails to prevent cyberattacks against IIoT networks. Recent secure protocols, such as secure distributed network protocol (DNP 3.0), are limited to weak hash functions for critical response time requirements. As a complementary, we propose an adaptive trust boundary protection for IIoT networks using a deep-learning, feature-extraction-based semisupervised model. Our proposed approach is novel in that it is compatible with multilevel protocols of IIoT. The proposed approach does not require any manual effort to update the attack databases and can learn the rapidly changing natures of unknown attack models using unsupervised learnings and unlabeled data from the wild. Therefore, the proposed approach is resilient to emerging cyberattacks and their dynamic nature. The proposed approach has been verified using a real IIoT testbed. Extensive experimental analysis of the attack models and results shows that the proposed approach significantly improves the identification of attacks over conventional security control techniques.

Automated summary

The rapid development of IoT platforms in the industrial domain has brought critical solutions, but also exposed industrial systems to cyber risks. An adaptive trust boundary protection approach for IIoT networks, utilizing deep learning and feature extraction, has been proposed and shown to significantly improve attack identification.