Multi-Layer Attack Graph Analysis in the 5G Edge Network Using a Dynamic Hexagonal Fuzzy Method

Area of Science:

• Telecommunications Engineering
• Network Security
• Cybersecurity Analytics

Background:

• Fifth-generation (5G) networks, built on software-defined networking, offer enhanced flexibility and scalability via network slicing.
• Despite advancements, 5G cybersecurity faces significant risks from network and connected device vulnerabilities.
• Existing vulnerability assessment frameworks lack real-time capabilities for dynamic 5G Edge environments.

Purpose of the Study:

• To develop and validate an optimized dynamic method for assessing 5G network vulnerabilities.
• To address the limitations of current frameworks in analyzing real-time scalability and the dynamic nature of 5G Edge networks.
• To enhance the accuracy of vulnerability analysis in 5G networks.

Main Methods:

• Integration of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with hexagonal fuzzy numbers.
• Development of an optimized dynamic method considering network vulnerabilities and dynamic factors like latency and accessibility.
• Utilizing a 5G testbed for method validation and comparison.

Main Results:

• The proposed method accurately analyzes vulnerabilities in 5G networks by identifying potential attack paths.
• Quantification of attack costs and network security levels was achieved.
• The optimized method demonstrated superior performance compared to classical TOPSIS and the Nessus vulnerability scanner.

Conclusions:

• The developed optimized dynamic method provides a robust solution for real-time 5G Edge network vulnerability assessment.
• This approach enhances cybersecurity by accurately identifying and quantifying risks in dynamic 5G environments.
• The findings contribute to securing critical IT infrastructure reliant on 5G technology.