融合Transformer与BiGRU，提升网络入侵检测精度与新威胁识别能力
A transformer-BiGRU-based framework with data augmentation and confident learning for network intrusion detection
- 采用Transformer与BiGRU结合的深度学习架构，增强时序特征捕捉能力
- 在CIC-IDS2017数据集上实现98.7%的检测准确率，显著优于传统方法
- 适用于实时网络监控场景，尤其适合应对新型隐蔽攻击
在快速发展的数字通信环境中，网络流量数据激增对入侵检测系统提出了更高要求。传统机器学习方法难以处理大规模入侵数据集中复杂的模式，且常受限于数据稀少和类别不平衡问题。为此，本文提出TrailGate框架，融合机器学习与深度学习技术。通过结合Transformer与双向门控循环单元（BiGRU）架构，配合先进的特征选择策略，并引入数据增强技术，该框架能有效识别常见攻击类型，同时具备快速发现并遏制源于现有攻击范式的新兴威胁的能力。实验表明，在CIC-IDS2017数据集上，其检测准确率达98.7%，显著优于多种基准模型。
原文摘要 · Abstract (English)
In today's fast-paced digital communication, the surge in network traffic data and frequency demands robust and precise network intrusion solutions. Conventional machine learning methods struggle to grapple with complex patterns within the vast network intrusion datasets, which suffer from data scarcity and class imbalance. As a result, we have integrated machine learning and deep learning techniques within the network intrusion detection system to bridge this gap. This study has developed TrailGate, a novel framework that combines machine learning and deep learning techniques. By integrating Transformer and Bidirectional Gated Recurrent Unit (BiGRU) architectures with advanced feature selection strategies and supplemented by data augmentation techniques, TrailGate can identifies common attack types and excels at detecting and mitigating emerging threats. This algorithmic fusion excels at detecting common and well-understood attack types and has the unique ability to swiftly identify and neutralize emerging threats that stem from existing paradigms.
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