Ontology-driven energy-efficient SAID framework for 5G and IoT networks using cryptographic techniques

The rapid deployment and usage of 5G and IoT networks in smart cities present significant challenges for cybersecurity, particularly regarding energy efficiency and attack detection. Existing intrusion detection systems often fail to balance high detection accuracy with low energy consumption, especially in resource-constrained environments. This research paper proposes an ontology-driven novel computational framework for energy-efficient Security Attack Identification and Detection (SAID) in 5G and IoT networks. The research framework proposed is a combination of cryptographic algorithms and semantic reasoning of ontologies to efficiently detect both known and unknown attacks, as well as to optimise energy consumption. The ontology provides a formalised semantic foundation for modelling attacks, vulnerabilities, network elements, and system states. Through this capability, the contextual knowledge can be modelled, thus allowing for context advancement and integrated attack detection. Through anomaly-based detection and machine learning techniques, cryptographic algorithms for secure communication and attack detection are made possible through a hybrid solution. Using machine learning algorithms to profile the network and to identify the anomalies, the hybrid attack detection is developed, while cryptographic algorithms can monitor the integrity and confidentiality of data exchange. After that, ever-changing. Experiments conducted using the CICIDS 2017 dataset demonstrate that the proposed framework improves attack detection accuracy by 22% and reduces energy consumption by 38% compared to traditional IDS solutions. The research paper outcome confirms the effectiveness of the proposed solution in energy-constrained environments, offering a scalable and robust method for cybersecurity in 5G and IoT networks. This work advances the integration of cryptographic solutions and ontology-based models in smart city applications, providing a path for future research on energy-efficient cybersecurity.