Article: Empowering intrusion detection in 5G embedded and cyber-physical networks Journal: International Journal of Embedded Systems (IJES) 2023 Vol.16 No.5/6 pp.401 - 412 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

Title: Empowering intrusion detection in 5G embedded and cyber-physical networks

Authors: Nitesh Singh Bhati; Manju Khari

Addresses: University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Delhi, 110078, India ' Jawahar Lal Nehru University, Delhi, India

Abstract: 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.

Keywords: intrusion detection technique; 5G embedded; cyber-physical network; machine learning; CatBoost.

DOI: 10.1504/IJES.2023.141945

International Journal of Embedded Systems, 2023 Vol.16 No.5/6, pp.401 - 412

Received: 09 Aug 2023
Accepted: 26 Jan 2024
Published online: 03 Oct 2024 *

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Title: Empowering intrusion detection in 5G embedded and cyber-physical networks

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