Empowering intrusion detection in 5G embedded and cyber-physical networks
Nitesh Singh Bhati, Manju Khari · International Journal of Embedded Systems · 2023
As intrusion detection systems (IDS) continue to evolve in response to emerging threats to edge devices and embedded devices, various approaches, such as anomaly-based and fuzzy logic-based techniques, have been employed to construct effective IDSs. More recently, with the introduction of 5G to the public usage, the data is dynamic and heterogeneous in nature due to which the integration of machine learning methodologies has gained prominence in IDS development. This research paper introduces a novel ensemble-based approach for enhancing intrusion detection within the context of modern 5G embedded and cyber-physical networks' security. The proposed technique leverages an optimised CatBoost classifier to fortify the defences of contemporary networks against potential breaches. To evaluate the efficacy of the proposed approach, experimentation was conducted using the KDDCup99 dataset. The results yielded by the proposed technique exhibit a remarkable 99.96% accuracy in detecting intrusions. This research contributes valuable insights to the realm of 5G embedded and cyber-physical systems by leveraging an ensemble-based approach with a focus on CatBoost optimisation. This study advances the field's understanding of bolstering intrusion detection capabilities within the evolving landscape of modern distributed networks.