Detection For Software Defined Networking With Machine Learning - 9783203253435

As the use of Software Defined Networking (SDN) becomes increasingly prevalent, securing these dynamic and programmable network architectures becomes paramount. The "Design of Intrusion Detection System for Software Defined Networking Using Machine Learning Algorithms" offers a groundbreaking solution to safeguard SDN environments against potential threats and attacks. This innovative Intrusion Detection System (IDS) leverages the power of machine learning algorithms to continuously monitor and analyze network traffic, behavioral patterns, and anomalies in real-time. By learning from historical data and network behaviors, the system can accurately identify deviations and malicious activities, enabling swift responses to potential intrusions. The integration of machine learning algorithms empowers the IDS to adapt to evolving threats, ensuring a proactive defense strategy and reducing the risk of false positives. As a result, network administrators can stay one step ahead of attackers a