Edinburgh Napier University

  Recent advancements in Software-Defined Networking (SDN) have facilitated its deployment across diverse network types, including edge networks. Given the broad applicability of SDN and the complexity of large-scale environments, establishing a comprehensive real-world test environment is both challenging and expensive. To circumvent these obstacles, software-based simulations are typically employed to validate solutions prior to real-world deployment. However, these simulations often do not incorporate real-time hardware data, limiting their realism. This paper introduces a novel hybrid SDN simulation testbed that integrates real hardware data within a Mininet-emulated network, addressing this limitation. To demonstrate the efficacy of our hybrid testbed, we present a specific scenario involving the dynamic allocation of edge resources to various client requests through a machine learning approach. This scenario focuses on detecting LiDAR spoofing attacks within automotive systems. Additionally, our hybrid testbed facilitates the generation and replication of new datasets for tailored scenarios, enhancing research capabilities in more intricate contexts.