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AI-enabled cybersecurity framework for future 5G wireless infrastructures.

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Summary
This summary is machine-generated.

This study introduces a multi-layered cybersecurity framework for fifth-generation (5G) networks, enhancing security with AI-driven threat detection and proactive measures. The framework achieves high threat detection rates and maintains performance, securing 5G infrastructures against evolving cyber threats.

Keywords:
5G securityAIFederated learningIntrusion detectionNetwork slicingZero trust

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Area of Science:

  • Cybersecurity
  • Network Engineering
  • Computer Science

Background:

  • Fifth-generation (5G) wireless networks offer enhanced connectivity but introduce significant cybersecurity vulnerabilities due to technologies like network slicing, edge computing, and IoT integration.
  • Existing security models are often reactive, failing to adequately address the expanded attack surface of complex 5G infrastructures.

Purpose of the Study:

  • To propose and evaluate a comprehensive, multi-layered cybersecurity framework designed for 5G infrastructures.
  • To implement security-by-design principles for proactive threat mitigation in 5G environments.

Main Methods:

  • Development of a multi-layered framework incorporating device-level trust validation, secure network slicing, dynamic policy enforcement, and AI-driven threat detection.
  • Evaluation through extensive simulations and benchmarking against NIST Zero Trust Architecture and 3GPP TS 33.501 standards.

Main Results:

  • Achieved a threat detection rate of up to 97.6% with low-latency performance under high-load and adversarial conditions.
  • Demonstrated scalable operation supporting large-scale device connectivity in simulated 5G environments.

Conclusions:

  • The proposed framework offers effective end-to-end protection for 5G networks, outperforming traditional reactive security models.
  • Future research should focus on quantum-resilient cryptography and AI-enhanced self-healing mechanisms for 6G networks.