# Multi-Layer Attack Graph Analysis in the 5G Edge Network Using a Dynamic Hexagonal Fuzzy Method
## 8 Conclusions and Future Work  
The 5G system improves the bandwidth and capabilities of the current telecommunication infrastructure. However, it introduces new threats and attacks. In this paper, we introduced a scalable and accurate vulnerability analysis approach that was tested and evaluated using our newly developed 5G Edge testbed. The experiment results depict that VAA successfully analyzed the vulnerabilities with a low error rate. The VAA using the classical TOPSIS, on average, is 31.35% more VEA-ble than Nessus. Whereas, the VAA using the Hexagonal Fuzzy TOPSIS method, on average, is 9.65% and 37.84% more VEA-ble than the VAA with the classical TOPSIS and Nessus, respectively. From a performance perspective, the VAA using the classical TOPSIS outperforms Nessus and the VAA using the Hexagonal Fuzzy TOPSIS by 27.14% and 11.15%, respectively. This is due to the Hexagonal fuzzy number computational time. The VAA using the Hexagonal Fuzzy TOPSIS is more scalable than the other methods when it is used in a large-scale 5G network. In future work, we will integrate the VAA with an autonomous intrusion response system that considers the vulnerability assessment values of VAA to deploy countermeasures against cyberattacks. We will also integrate the model with a secure network slicing approach to decide which resources can be used by the network slices based on their risk assessment evaluation and block resources that are under attack. This research was generously supported in part by the SUNY Polytechnic Institute Research Seed Grant Program. This research was also supported in part by the Air Force Research Laboratory through the Information Directorate's Information Institute(r) CP0# FA8750-20-3-1003 contract # SA1003202109E0410. Figure 14: The scalability of the VAA and Nessus using a variant number of UEs. * (23) [PERSON]; [PERSON] A fuzzy MCDM approach for personnel selection. _Expert Syst. Appl._**2010**, _37_, 4324-4330. [CrossRef]
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