用改进生成对抗网络检测物联网中的异常流量，效果更好且适合边缘部署。
SD-CGAN: Conditional Sinkhorn Divergence GAN for DDoS Anomaly Detection in IoT Networks
- 结合条件生成网络与几何感知损失函数，提升训练稳定性。
- 在CICDDoS2019数据集上实现高精度检测，召回率和F1值领先。
- 适合资源受限的物联网边缘环境，兼具性能与效率。
物联网边缘网络日益复杂，给异常检测带来挑战，尤其在动态、不平衡的流量条件下识别复杂的拒绝服务（DoS）攻击和零日漏洞。本文提出一种基于Sinkhorn散度的条件生成对抗网络框架SD-CGAN，专为物联网边缘环境设计。该框架采用基于CTGAN的合成数据增强以缓解类别不平衡问题，并引入几何感知的Sinkhorn散度作为损失函数，提升训练稳定性和减少模式崩溃。模型在CICDDoS2019数据集中针对恶意攻击子集进行评估，相比基线深度学习与GAN方法，在检测准确率、精确率、召回率和F1分数上均表现更优，同时保持适用于边缘部署的计算效率。
原文摘要 · Abstract (English)
The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated Denial-of-Service (DoS) attacks and zero-day exploits under highly dynamic and imbalanced traffic conditions. This paper proposes SD-CGAN, a Conditional Generative Adversarial Network framework enhanced with Sinkhorn Divergence, tailored for robust anomaly detection in IoT edge environments. The framework incorporates CTGAN-based synthetic data augmentation to address class imbalance and leverages Sinkhorn Divergence as a geometry-aware loss function to improve training stability and reduce mode collapse. The model is evaluated on exploitative attack subsets from the CICDDoS2019 dataset and compared against baseline deep learning and GAN-based approaches. Results show that SD-CGAN achieves superior detection accuracy, precision, recall, and F1-score while maintaining computational efficiency suitable for deployment in edge-enabled IoT environments.
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。