A Ranking Method of Hexagonal Fuzzy Numbers Based on Their Possibilistic Mean Values

Leela-apiradee, Worrawate; Thipwiwatpotjana, Phantipa

doi:10.1007/978-3-030-21920-8_29

Cited by 1 publication

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“…A fuzzy number M ~ is an HFN denoted by six tuples M ~ ( a 1 , a 2 , a 3 , a 4 , a 5 , a 6 , r , s ) where a 1 ≤ a 2 ≤ a 3 ≤ a 4 ≤ a 5 ≤ a 6 are real numbers and its membership function µM ~ ( x ) is given below in Equation (1), where and 0 < r , s < 1 are interval values of the µM ~ ( x ). The graphical representation of a HFN for x ∈ [0, 1] is shown in Figure 1 [ 25 , 49 ].

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Section: Background and Related Workmentioning

confidence: 99%

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

Kholidy

2021

Sensors

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Overall, 5G networks are expected to become the backbone of many critical IT 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.

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Section: Background and Related Workmentioning

confidence: 99%