Intrusion detection in software defined networks with imbalanced attack classes

Software Defined Networks (SDNs) have revolutionized the way modern networks are managed and orchestrated. This sophisticated infrastructure can provide numerous benefits but at the same time introduce several security challenges. A centralized controller holds the responsibility of managing the network traffic, thus making it an attractive target to attackers. Intrusion Detection Systems (IDSs) play a crucial role in identifying and addressing security threats within the SDN. In this paper, we developed an SDN-IDS system by utilizing machine learning techniques for anomaly detection to identify deviations in network behavior. This is specifically challenging due to the fact that we only have a few samples from several of the attack classes, i.e. minority classes. Five machine learning algorithms were employed to train the SDN-IDS, and ultimately the most appropriate one was chosen. Moreover, we applied the SMOTE and Tomek link re-samplings on the dataset as well as a cost-sensitive learning technique to enhance the classification performance of the minority attacks. The Decision Tree (DT) model, trained on a feature-reduced and resampled dataset using cost-sensitive learning, achieved an impressive overall performance with 99.87% accuracy and an F1-score of 99.87. Additionally, it demonstrated a classification accuracy above 99% in identifying 11 out of the 15 possible traffic classes.