# Multi-Layer Attack Graph Analysis in the 5G Edge Network Using a Dynamic Hexagonal Fuzzy Method
###### Abstract  
Overall, 5G networks are expected to become the backbone of many critical \(\Pi\) applications. With 5G, new tech advancements and innovation are expected; 5G currently operates on software-defined networking. This enables 5G to implement network slicing to meet the unique requirements of every application. As a result, 5G is more flexible and scalable than 4G LTE and previous generations. To avoid the growing risks of hacking 5G cybersecurity needs some significant improvements. Some security concerns involve the network itself, while others focus on the devices connected to 5G. Both aspects present a risk to consumers, governments, and businesses alike. There is currently no real-time vulnerability assessment framework that specifically addresses 5G Edge networks, with regard to their real-time scalability and dynamic nature. This paper studies the vulnerability assessment in the 5G networks and develops an optimized dynamic method that integrates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the hexagonal fuzzy numbers to accurately analyze the vulnerabilities in 5G networks. The proposed method considers both the vulnerability and 5G network dynamic factors such as latency and accessibility to find the potential attack graph paths where the attack might propagate in the network and quantifies the attack cost and security level of the network. We test and validate the proposed method using our 5G testbed and we compare the optimized method to the classical TOPSIS and the known vulnerability scanner tool, Nessus. attack graphs; dynamic vulnerability analysis; hexagonal fuzzy number; 5G Edge security; decision-making technique; 5G security testbed +
Footnote †: journal: Sensors  
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Footnote 1: email: {[PERSON], H.  
change can be viewed with some trepidation since, until now, telecom protocols were closed, making them an entry barrier to attackers. Conversely, Internet technologies are open, and they are well known. This emphasizes the need for robust security mechanisms across the entire 5G network. According to the national strategy to secure 5G implementation plan that was developed by the National Telecommunications and Information Administration (NTIA), the following challenges should be considered to develop an efficient security solution for 5G networks [3]:  
1. The 5G network supports heterogenous infrastructure. Any security solution should use a combination of centralized and distributed, physical and virtual deployments to ensure security at multiple levels (e.g., slices, services, and resources) across multiple domains (i.e., administrative and technological domains where 5G services are orchestrated such as Mobile Virtual Network Operators (MVNOs) for automotive, eHealth, massive IoT, massive multimedia broadband, etc. [4]). 2. The 5G network requires scalable and higher-performance security solutions. The increase in bandwidth from 4G eNodeB to 5G gNodeB will cause significant increases in performance and scale requirements that the current security infrastructure may not be able to handle. This means the threat detection and vulnerability analysis solutions should be dynamic, consistent, and scalable. 3. Distributed edge clouds create new attacks surfaces and vulnerability points. If no proper security mechanisms are in place, such as encryption or firewalls, IP connectivity will terminate at the edge of the operator. As a result, edge cloud nodes become susceptible to spoofing, eavesdropping, and other attack [5]. 4. Virtualization and network slicing bring new risks. There must be a method for separating virtualization layers and network slices from one another [6; 7; 8]. To the best of our knowledge, none of the current works introduces a real-time vulnerability assessment framework that specifically works for 5G Edge networks and considers these systems' real-time scalability and dynamic features due to the lack of publicly available 5G Edge testbeds, datasets, and attack graphs.