Dynamic Network Slicing and Deep Learning Based Intrusion Detection System with Virtual Load Balancing in Edge Enabled SDN/NFV Based 5G Networks
Asad Faraz Khan, Priyadarsi Nanda · 2024
In current times, network slicing in a 5G context is a significant study field. But it might be difficult to meet network slice requests' requirements. Network slices need to share limited resources; therefore energy efficiency and security are crucial. Additionally, it is essential to establish secured network slicing for Software-Defined Network/Network Function Virtualization (SDN/NFV). As attackers have developed to become more skilled and frequently use different attacking approaches, security is a crucial problem in network slicing. We address security, ineffective network slicing, and overloading using load balancing and Deep Learning (DL) based network slicing algorithms in edge enabled SDN/NFV assisted 5G settings in this research. Here, we mainly concentrate on secure and efficient network slicing in SDN/NFV assisted 5G systems. Initially, slicing of network is performed based on UniqueNet which includes lightweight convolutional layers that reduce the processing time and increase accuracy. For authentication of users, we employ the Improved Mersenne Twister (IMT) algorithm and role-based access control is performed using Improved Deep Q Network (ImDQN) algorithm for authorization purpose. Clustering is done by using k-means clustering (KMC) algorithm. Here, Cluster Head (CH) performed intrusion detection using Enhanced Bidirectional Generative Adversarial Network (E-BiGAN) algorithm. After detected intrusions, the Kangaroo-based IDS (KIDS) jump and send the notification to all the nodes in the CH. For efficient load balancing, we perform optimal switch selection using Dove Swarm Optimization (DSO). The performance of the suggested framework is then evaluated in terms of different metrics and compared with existing approaches to prove the efficacy of the proposed system.