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

Area of Science:

• Cybersecurity
• Network Security
• Smart City Technologies

Background:

• 5G and IoT networks in smart cities face cybersecurity challenges, especially balancing attack detection with energy efficiency.
• Traditional intrusion detection systems struggle in resource-constrained environments.

Purpose of the Study:

• To propose an ontology-driven computational framework for energy-efficient Security Attack Identification and Detection (SAID) in 5G and IoT networks.
• To address the limitations of existing systems in achieving both high detection accuracy and low energy consumption.

Main Methods:

• A hybrid solution combining cryptographic algorithms and semantic reasoning of ontologies.
• Utilizing machine learning for network profiling and anomaly detection.
• Employing ontologies for formalizing attacks, vulnerabilities, and system states for integrated detection.

Main Results:

• Improved attack detection accuracy by 22% compared to traditional Intrusion Detection Systems (IDS).
• Reduced energy consumption by 38% in experiments.
• Demonstrated effectiveness in energy-constrained environments.

Conclusions:

• The proposed framework offers a scalable and robust method for cybersecurity in 5G and IoT networks.
• Advances the integration of cryptographic solutions and ontology-based models for energy-efficient cybersecurity in smart cities.