Comprehensive Security of SDN Controllers in NFVI-Based 5G Network

In the present world, Software-Defined Networks (SDN) is the developing technological environment that allows integrated control and splits the control plane from the data plane. It is essential to classify the attacks in SDN-based networks to improve security. Concomitantly, SDN-related networks are vulnerable to numerous attacks especially Distributed Denial of Service (DDoS) attacks which interrupt the data transmission and network data. To resolve this issue, various traditional researchers have attempted to attain attack detection, however, there is a lack in computational cost, accuracy rate, and Packet Delivery Ratio. To overcome the limitations, the proposed hybrid model employs a system creation model for encrypting data using ELGAMAL and ECC algorithms to strengthen data security. Furthermore, the present hybrid model incorporates the wrapper-based approach Sine Cosine hybrid optimization algorithm with Modified Particle Swarm Optimization (SCMPSO) for feature selection which is intended to improve the performance of the classification. Furthermore, it utilizes the XG Boost-Light Gradient-Boosting Machine (GBM) algorithm for attack classification. Accordingly, Extreme Gradient-Boosting (XG Boost) can handle the missing data, and the ensemble nature of XG Boost with multiple Decision trees (DT) combinations makes it challenging to finalize the prediction. To overcome the limitations of the XG Boost algorithm, the proposed model uses Light GBM. Correspondingly, the present method created a dataset using the Mininet tool. The efficacy of the proposed hybrid model is calculated with several evaluation metrics to analyze the performance. The proposed method is intended to contribute to SDN-based network development and is envisioned to contribute to attack detection mechanisms.