Deep-Learning-Based Weak Electromagnetic Intrusion Detection Method for Zero Touch Networks on Industrial IoT

The Industrial Internet of Things (IIoT), consisting of a large number of self-organized sensors, is one of the prominent representatives of zero touch networks, which will be widely used for information interconnection. With the advancement in intelligent manufacturing, the security of zero touch IIoT becomes a critical issue in various applications. One of the main factors that endanger the normal operation of zero touch IIoT is the weak electromagnetic interference (WEMI) attack, making special precautions necessary for zero touch IIoT. In real-life applications, sensors will be injected with a specific type of noise due to the unique manufacturing process and environment. This noise can be considered as the finger-print of the sensor, which is stable under normal conditions unless the sensor experiences a WEMI attack. Hence, a deep-learning-based WEMI intrusion detection method is employed in this study. First, we introduce the application of Kalman and moving average filters in the fingerprint extraction stage. Second, the frequency and time domain features were extracted from the fingerprint. Third, deep learning models are applied to intrusion detection, and a cloud-edge-end computing framework is proposed. Finally, the experiment analyzes the performance of the WEMI intrusion detection method.

Automated summary

This study employs a deep learning-based method for WEMI intrusion detection, extracting sensor fingerprints using Kalman and moving average filters, and extracting frequency and time domain features. Experimental results show that this method performs well in WEMI intrusion detection.