Artificial Intelligence Driven Intrusion Detection for SDN Environments

Software-defined networking (SDN) is an innovative methodology that enables direct programmability of network control, while abstracting the underlying infrastructure from applications and network services. Control plane. The centralization required for security is susceptible to several cyber dangers not often seen in other network topologies. The researchers devised an innovative machine-learning technique to detect infections inside networks. We used the classifier on the UNSW-NB 15 intrusion detection benchmark and developed a model using this data. Random Forest and Decision Tree are classifiers used in conjunction with Gradient Boosting and AdaBoost. Among the top-performing models, Gradient Boosting achieved an accuracy of 99.87%, a recall of 100%, and an F1 score of 99.85%, making it dependable for intrusion detection in SDN networks. The second most effective classifier was Random Forest, with an accuracy of 99.38%, followed by AdaBoost and Decision Tree. The study indicates that Gradient Boosting’s effectiveness in this job stems from the integration of weak learners to form a robust ensemble model capable of accurately predicting whether traffic is normal or malicious. This study demonstrates that the GBDT-IDS model considerably enhances network security and has superior characteristics regarding real-time detection accuracy and minimal false positive rates. In next endeavors, we will include this model into the real SDN environment to assess its applicability and scalability. This study establishes a foundational framework for advancing security in Software-Defined Networking via machine learning approaches, hence fostering more secure and resilient networks.