Abstract: To meet the challenge of widely existing and frequently changing network attacks, intrusion detection systems (IDSs) are introduced to recognize intrusions and to protect computer networks. Among all these IDSs, conventional machine learning methods rely on shallow learning and have unsatisfactory performance. Unlike machine learning methods, deep learning methods are the mainstream methods because of their capability to handle mass data without prior knowledge of specific domain expertise. Concerning deep learning, long short-term memory (LSTM) and temporal convolutional networks (TCNs) can be used to extract temporal features from different angles, while convolutional neural networks (CNNs) are valuable for learning spatial properties. Based on the above, this paper proposes a novel interlaced and spatiotemporal deep learning model called Cnn Rnn Gated Tcn-self attention (CRGT-SA), which combines the CNN with Gated TCN and RNN (LSTM) modules to learn spatiotemporal properties, and imports the self-attention mechanism to select significant features. More specifically, our proposed model splits the feature extraction into multiple steps with a gradually increasing granularity, and executes each step with combined CNN, LSTM, and Gated TCN modules. Our proposed CRGT-SA model is validated using the UNSW-NB15 data set and compared with other compelling techniques, including traditional machine learning and deep learning models as well as state-of-the-art deep learning models. According to the simulation results, our proposed model exhibits the highest accuracy and F1-score among all the compared methods. More specifically, our proposed model achieves 91.5% and 90.5% accuracy for binary and multi-class classifications, respectively, and demonstrates its ability to protect the Internet from complicated network attacks. Moreover, we conducted another series of experiments on the NSL-KDD data set; the simulation results of comparison with other models further prove the generalization ability of our proposed model.