As the scale of the Industrial Internet continues to expand, the number of network connections and data traffic are experiencing explosive growth. Security threats and attack types targeting the Industrial Internet are becoming increasingly complex, rendering traditional firewalls and encryption/decryption technologies inadequate for addressing diverse and sophisticated attack scenarios. Furthermore, traffic characteristics within the Industrial Internet environment exhibit significant asymmetry, such as a highly imbalanced distribution between benign and malicious traffic. To address this challenge, this paper proposes CBiNet—a hybrid deep learning model that integrates a one-dimensional convolutional neural network (1D-CNN) with a bidirectional long short-term memory network (BiLSTM). Designed to effectively learn and leverage such asymmetric spatio-temporal patterns, experimental validation demonstrates that the CBiNet model can efficiently tackle complex traffic identification tasks in industrial internet environments. It provides a highly accurate, scalable intrusion detection method for securing industrial internet gateways.

Etching has become a critical step in semiconductor wafer fabrication, and its importance in semiconductor manufacturing highlights the fact that it directly determines the ability of the fab to produce high-process products, as well as the application performance of the chip. While the health of the etcher is a concern, especially for the cooling system, accurately predicting the remaining useful life (RUL) of the etcher cooling system is a critical task. Predictive maintenance (PDM) can be used to monitor the basic condition of the equipment by learning from historical data, and it can help solve the task of RUL prediction. In this paper, we propose the FECAM-WTCN-Informer model, which first obtains a new WTCN structure by inserting wavelet convolution into the TCN, and then combines the discrete cosine transform (DCT) and channel attention mechanism into the temporal neural network (TCN). Multidimensional feature extraction of time series data can be realized, and the processed features are input into the Informer network for prediction. Experimental results show that the method is significantly more accurate in terms of overall prediction performance (MSE, RMSE, and MAE), compared with other state-of-the-art methods, and is suitable for solving the problem of predictive maintenance of etching machine cooling systems.