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.

翻译：网络入侵检测对于保障现代网络安全至关重要，然而网络流量的复杂性给传统方法带来了重大挑战。本研究提出了一种基于时序卷积网络（TCN）的模型，该模型采用具有扩张卷积的残差块架构，以捕获网络流量数据中的依赖关系，同时确保训练稳定性。与循环神经网络相比，TCN能够并行处理序列，从而实现更快、更准确的序列建模。在Edge-IIoTset数据集（包含正常流量及14种网络攻击类型共15个类别）上的评估表明，所提模型取得了96.72%的准确率和0.0688的损失值，其性能优于一维CNN、CNN-LSTM、CNN-GRU、CNN-BiLSTM及CNN-GRU-LSTM等模型。涵盖召回率、精确率、准确率和F1分数等指标的逐类别分类报告显示，TCN模型在恶意软件、注入攻击和分布式拒绝服务攻击等多种攻击类别上均表现出优越性能。这些结果凸显了该模型在有效应对网络入侵检测复杂性方面的潜力。