To address the core issue of insufficient modeling of multi-time-scale temporal features in modern network attack detection, this study proposes a network attack detection model based on Clock-Work Recurrent Neural Network (CW-RNN). The model introduces a time-division processing mechanism, divides hidden layer neurons into modular structures with different clock frequencies, and integrates an attention mechanism to enhance feature extraction in key attack stages. An end-to-end intelligent detection architecture is constructed, which can adaptively capture characteristics of both short-term burst attacks and long-term latent attacks. Experimental verification is conducted on two benchmark datasets: University of New South Wales Network Benchmark 2015 (UNSW-NB15) and Canadian Institute for Cybersecurity-Intrusion Detection Systems 2018 (CSE-CIC-IDS2018). The results show that the detection accuracies of the proposed CW-RNN model on the two datasets reach 95.8% and 95.2%, respectively, and the macro-average F1-scores are 94.2% and 93.6%, respectively, which are significantly superior to mainstream benchmark models such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Meanwhile, the training time of the model is reduced by 25%, and the inference speed is increased by more than 18%, achieving dual optimization in detection accuracy and computational efficiency. Its modular multi-time-scale processing design provides an efficient and practical technical solution for network attack detection.
Lin et al. (Tue,) studied this question.