NetVigil: Robust and Low-Cost Anomaly Detection for East-West Data Center Security
Kevin Hsieh, Mike Wong, Santiago Segarra, Sathiya Kumaran Mani, Trevor Eberl, Anatoliy Panasyuk, Ravi Netravali, Ranveer Chandra, Srikanth Kandula
摘要
The growing number of breaches in data centers underscores an urgent need for more effective security. Traditional perimeter defense measures and static zero-trust approaches are unable to address the unique challenges that arise from the scale, complexity, and evolving nature of today's data center networks. To tackle these issues, we introduce NetVigil, a robust and cost-efficient anomaly detection system specifically designed for east-west traffic within data center networks. NetVigil adeptly extracts security-focused, graphbased features from network flow logs and employs domainspecific graph neural networks (GNNs) and contrastive learning techniques to strengthen its resilience against normal traffic variations and adversarial evasion strategies. Our evaluation, over various attack scenarios and traces from real-world production clusters, shows that NetVigil delivers significant improvements in accuracy, cost, and detection latency compared to state-of-the-art anomaly detection systems, providing a practical, supplementary security mechanism to protect the east-west traffic within data center networks.
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引用它的顶会 Paper6
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它引用的顶会 Paper14
- Graph Contrastive Learning with AugmentationsYuning You, Tianlong Chen, Yongduo Sui, Ting Chen 等NeurIPS 2020 · 被引用 3,042 次
- Strategies for Pre-training Graph Neural NetworksWeihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik 等ICLR 2020 · 被引用 1,744 次
- Contrastive Multi-View Representation Learning on GraphsKaveh Hassani, Amir Hosein Khas AhmadiICML 2020 · 被引用 1,663 次
- Graph Contrastive Learning with Adaptive AugmentationYanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu 等WWW 2021 · 被引用 1,415 次
- InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information MaximizationFan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian TangICLR 2020 · 被引用 1,010 次
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