SDN based Firewall with Real Time Policy Updates

The growing complexity of contemporary network networks and the rise of Software-Defined Networking (SDN) introduce new difficulties in network protection. Classical firewalls cannot respond to dynamic online threats because of dependence on static policy rules, thereby exposing networks to advanced attacks. In SDNs, the availability of vulnerabilities of the control plane allows malicious attacks to be created, causing service outages and data leakage. The paper introduces an SDN-Based Firewall with Real-Time Policy Updates, which incorporates machine learning algorithms (Random Forest, K-Nearest Neighbors) for adaptive threat detection and dynamic security policy enforcement. Through ongoing network traffic monitoring and heuristic-based packet inspection, the system categorizes threats in real-time and updates firewall rules autonomously to counter attacks. The SDN controller enforces security policies centrally, providing network-wide protection with ease. This solution majorly improves intrusion detection, lowers response time to threats, and offers a scalable, intelligent, and automated firewall solution for contemporary network infrastructures. The system not only enhances cybersecurity resilience but also provides a paradigm shift towards proactive, AI-based network defense systems.