Network intrusion detection is critical for securing modern networks, yet the complexity of network traffic poses significant challenges to traditional methods. This study proposes a Temporal Convolutional Network(TCN) model featuring a residual block architecture with dilated convolutions to capture dependencies in network traffic data while ensuring training stability. The TCN's ability to process sequences in parallel enables faster, more accurate sequence modeling than Recurrent Neural Networks. Evaluated on the Edge-IIoTset dataset, which includes 15 classes with normal traffic and 14 cyberattack types, the proposed model achieved an accuracy of 96.72% and a loss of 0.0688, outperforming 1D CNN, CNN-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-GRU-LSTM models. A class-wise classification report, encompassing metrics such as recall, precision, accuracy, and F1-score, demonstrated the TCN model's superior performance across varied attack categories, including Malware, Injection, and DDoS. These results underscore the model's potential in addressing the complexities of network intrusion detection effectively.
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