Res2Net-ERNN: deep learning based cyberattack classification in software defined network

Software-defined networking (SDN) is known for its enhanced network programmability and adaptability, but maintaining strong safety precautions to protect against emerging cyber-attacks remains a constant issue. Since SDN has logically centralized control, an attack on the controller might paralyze the entire network. For this reason, intrusion detection is very crucial. Many academics have embraced state-of-the-art techniques to assess and identify these assaults. However, the majority of these approaches lack scalability and accuracy. Moreover, they had difficulties with restricted features, low efficiency, incorrect characteristics, and computing complexity. Therefore, to detect network vulnerabilities in SDN-based IoT networks, we developed a practical deep learning approach based on Res2Net and Elman Recurrent Neural Networks (ERNN) technique as a defense solution to detect security issues in SDN. This framework consists of multiple steps and starts by addressing the dataset’s class imbalance issue with a Data Augmentation Generative Adversarial Network (DAGAN). Next, the Res2net and Enhanced Honey Badger Algorithm (EHBA) are used to extract and select features. This lowers the computational expense and lessens the possibility that the model would be misled by unsuitable and negative characteristics. Finally, an ERNN-based technique is used to detect and classify the intrusions in SDN. After seeing the network assaults, a practical mitigation framework is implemented to mitigate the network attacks. Three SDN IoT-focused datasets, InSDN, IoT-23 and ToN-IoT, are used in an experimental investigation to analyze the proposed framework’s performance. The results of numerous trials show that the proposed method outperforms existing techniques regarding several constraints.