用时序卷积网络提升网络入侵检测准确率
A Temporal Convolutional Network-based Approach for Network Intrusion Detection
- 采用带膨胀卷积的残差结构捕捉流量时序依赖
- 在Edge-IIoTset数据集上达96.72%准确率，损失0.0688
- 适合需要快速高精度检测的网络安全场景
网络入侵检测对保障现代网络安全至关重要，但网络流量的复杂性给传统方法带来挑战。本研究提出一种基于时序卷积网络（TCN）的模型，采用带膨胀卷积的残差块结构，在确保训练稳定性的同时捕捉网络流量中的时序依赖关系。由于TCN可并行处理序列，其序列建模速度与精度优于循环神经网络。在包含15类正常流量和14种网络攻击类型的Edge-IIoTset数据集上，该模型达到96.72%的准确率和0.0688的损失，优于1D CNN、CNN-LSTM、CNN-GRU、CNN-BiLSTM及CNN-GRU-LSTM模型。类别级分类报告表明，该模型在恶意软件、注入攻击和DDoS等各类攻击中均表现优异，验证了其在应对网络入侵检测复杂性方面的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。