Improving IIoT security: Unveiling threats through advanced side-channel analysis

The widespread deployment of IIoT edge devices makes them attractive victims for malicious activities. Consequently, how to implement trustworthy operations becomes a realistic topic in embedded systems. While most current physical systems for detecting malicious activities primarily focus on identifying known intrusion codes at the block level, they ignore that even an unnoticeable injected function can result in system-wide loss of security. In this paper, we propose a framework called CNDSW built on deep-learning side-channel analysis for function-level industrial control flow integrity monitoring. By collaboratively utilizing correlation analysis and deep-learning techniques, the dual window sliding monitoring mechanism in the proposed CNDSW framework demonstrates a real-time code intrusion tracking capacity on embedded controllers with a 99% detection accuracy on average. Instead of focusing on known block-level intrusions, we experimentally show that our model is feasible to detect function-level code intrusions without knowing the potential threat type. Besides, we further explore how different configurations of the CNDSW framework can help the monitoring process with different emphases and to which extent the model can concurrently detect multiple code intrusion activities. All our experiments are conducted on 32-bit ARM Cortex-M4 and 8-bit RISC MCUs across five different control flow programs, providing a comprehensive evaluation of the framework's capabilities.