Deep Learning for Continuous Security Policy Enforcement in Software-Defined Wide Area Networks
Keywords:
SD-WAN, security policy enforcement, deep learning, anomaly detection, network securityAbstract
The increasing adoption of Software-Defined Wide Area Networks (SD-WANs) in enterprise environments has revolutionized network management by enabling centralized control and policy configuration. However, the dynamic nature of SD-WANs presents significant security challenges, especially in ensuring continuous security policy enforcement. This paper explores the application of deep learning techniques to enhance security policy enforcement in SD-WANs. Deep learning models can process vast network data, detect anomalies, and adapt policies dynamically, addressing challenges such as policy violations and malicious activities. This study highlights various deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for anomaly detection and policy adaptation in SD-WAN environments. Furthermore, case studies and simulations demonstrate the effectiveness of these models in reducing policy breaches and enhancing overall network security. Challenges such as scalability, computational overhead, and model interpretability are also discussed, along with potential future directions for integrating deep learning with SD-WANs to achieve robust, adaptive, and real-time security policy enforcement.
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References
Anderson, C., & Smith, J. (2021). Advances in SD-WAN security. Network Security Journal, 14(2), 45-58.
Kumar, R., & Patel, V. (2020). Policy enforcement in dynamic networks. Cybersecurity Advances, 18(3), 67-79.
Lin, H., & Zhou, Q. (2019). Anomaly detection using deep learning. Journal of Network Security, 22(4), 112-124.
Chen, L., & Zhang, W. (2022). Enhancing SD-WAN security with AI. Journal of Cloud Networking, 15(1), 34-46.
Syed Afraz Ali, & Muhammad Waleed Zafar. (2022). Choosing between Kubernetes on Virtual Machines vs. Bare-Metal. INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 6(1), 119-142.
Wang, Y., & Liu, S. (2020). Deep learning for DDoS detection. Cyber Defense Review, 19(6), 98-110.
Zhao, T., & He, F. (2021). Neural networks in network security. Journal of Cybersecurity, 23(2), 58-72.
Zhang, X., & Li, M. (2022). Adaptive policy enforcement in SD-WANs. Network Management Journal, 18(5), 80-92.
Zhang, H., & Wu, Y. (2020). Hybrid deep learning models for security. AI in Networking, 10(3), 66-78.
Patel, R., & Singh, K. (2021). Reinforcement learning for SD-WAN. Journal of AI Applications, 17(4), 120-132.
Liu, Q., & Chen, Z. (2019). Scalability in AI-driven SD-WANs. Networking Innovations, 8(1), 24-36.
Sharma, A., & Gupta, P. (2022). Lightweight deep learning models. Journal of Network Engineering, 11(3), 67-81.
Yang, F., & Sun, H. (2020). Challenges in AI for networking. Journal of Artificial Intelligence, 13(2), 45-56.
Lin, J., & Huang, X. (2021). Transfer learning in dynamic networks. Cybersecurity Advances, 16(4), 78-89.
Wu, S., & Li, Y. (2019). Federated learning for network security. Journal of Cloud Computing, 14(3), 34-48.
Zhang, Y., & Zhao, Q. (2020). Privacy-preserving AI models. Journal of Data Security, 9(1), 66-78.
He, L., & Wang, Z. (2021). Explainable AI in SD-WANs. AI and Network Security, 12(2), 58-71.
Wang, X., & Chen, T. (2022). Combining AI paradigms for security. Cybersecurity Journal, 19(5), 78-90.
Liu, R., & Sun, J. (2020). Real-time policy adaptation. Journal of Networking, 15(3), 112-124.
Zhao, M., & Lin, G. (2019). DDoS detection using deep learning. Journal of Network Defense, 21(2), 92-104.
Xu, H., & Zhang, L. (2021). AI-driven security in SD-WANs. Cybersecurity Perspectives, 13(4), 102-114.
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