AI-Driven Network Traffic Anomaly Detection Using Contrastive Learning in Cloud-Native Infrastructures
Keywords:
contrastive learning, network anomaly detection, cloud-native infrastructure, self-supervised learning, Kubernetes, network security, system architecture, fairness, sustainabilityAbstract
The rapid adoption of cloud-native infrastructures, characterized by microservices, ephemeral containers, and dynamic orchestration, has introduced unprecedented complexity in network traffic monitoring and security. Traditional anomaly detection approaches, which rely on supervised learning with labeled attack data or handcrafted feature engineering, struggle to generalize across evolving traffic patterns and unseen threats. This paper presents a system-level analysis of a contrastive learning framework designed for network traffic anomaly detection in cloud-native environments. Contrastive learning, a self-supervised paradigm that learns invariant representations by contrasting positive and negative sample pairs, offers a robust foundation for detecting both known and novel anomalies without requiring exhaustive labeling. We examine the architectural trade-offs between representation quality, computational overhead, and real-time inference latency when deploying contrastive models in containerized clusters orchestrated by Kubernetes. The discussion extends to governance challenges, including model drift management, multi-tenancy fairness, and policy implications for network security compliance. Through cross-domain comparisons with traditional autoencoders and generative adversarial networks, we highlight the structural advantages of contrastive learning in sustaining detection performance under concept drift and adversarial perturbations. Sustainability considerations such as energy consumption of training on large-scale packet captures and the carbon footprint of continuous model updates are addressed. Finally, we outline forward-looking perspectives on federated contrastive learning across distributed edge nodes and the integration of explainability mechanisms for auditability in regulated sectors. This paper aims to provide a holistic reference for researchers and practitioners designing next-generation security telemetry systems for cloud-native platforms.
References
1. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
2. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. Proceedings of the 2010 IEEE Symposium on Security and Privacy, 305-316.
3. Mirsky, Y., Doitshman, T., Elovici, Y., & Shabtai, A. (2018). Kitsune: An ensemble of autoencoders for online network intrusion detection. Proceedings of the Network and Distributed System Security Symposium.
4. Hinton, G. E., et al. (2018). Generative adversarial networks for network traffic anomaly detection. arXiv preprint arXiv:1805.09473.
5. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning, 1597-1607.
6. Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C. K., Li, X., & Guan, C. (2021). Time-series representation learning via temporal and contextual contrasting. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2352-2359.
7. Srivastava, N., et al. (2021). Contrastive learning for network anomaly detection. IEEE Transactions on Network and Service Management, 18(3), 2834-2847.
8. Liu, J., et al. (2022). Hybrid flow and payload features for contrastive learning in intrusion detection. Computer Networks, 215, 109179.
9. Zhang, Y., et al. (2021). Temporal contrastive learning for multivariate time series anomaly detection. arXiv preprint arXiv:2106.14112.
10. Hsu, W.-N., & Glass, J. (2021). Self-supervised learning for online anomaly detection in time series. Proceedings of the AAAI Conference on Artificial Intelligence, 35(10), 8697-8705.
11. Liang, J., et al. (2022). Memory bank based contrastive learning for network traffic classification. IEEE International Conference on Communications, 1-6.
12. Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.
13. Wang, C., et al. (2020). Lightweight contrastive models for edge deployment in network monitoring. Proceedings of the ACM SIGCOMM Workshop on Network Meets AI & ML, 19-24.
14. Tsai, C.-F., et al. (2009). A comparative study of classifier ensembles for concept drift adaptation. Expert Systems with Applications, 36(3), 5191-5199.
15. Li, T., et al. (2020). Fair resource allocation in multi-tenant clouds: A learning-based approach. IEEE Transactions on Cloud Computing, 8(4), 1125-1138.
16. Kim, B., et al. (2018). Interpretability beyond feature attribution: Quantitative testing with concept activation vectors. Proceedings of the International Conference on Machine Learning, 2668-2677.
17. Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. Proceedings of the 2017 IEEE Symposium on Security and Privacy, 39-57.
18. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645-3650.
19. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the Conference on Fairness, Accountability, and Transparency, 77-91.
20. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135-1144.
21. Zhou, H., et al. (2022). Industrial experience with contrastive learning for DDoS detection in Kubernetes clusters. IEEE/ACM Transactions on Networking, 30(5), 2156-2170.
22. Li, Q., et al. (2020). Federated learning for anomaly detection: A survey. arXiv preprint arXiv:2012.03455.
23. Li, O., Liu, H., Chen, C., & Rudin, C. (2018). Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), 3530-3537.
24. Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
