Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan 29050, Khyber Pakhtunkhwa, Pakistan.
Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen.
Comput Intell Neurosci. 2021 Oct 31;2021:9427492. doi: 10.1155/2021/9427492. eCollection 2021.
In our lives, we cannot avoid the uncertainty. Randomness, rough knowledge, and vagueness lead us to uncertainty. In mathematics, the fuzzy set (FS) theory and logics are used to model uncertain events. This article defines a new concept of complex picture fuzzy relation (CPFR) in the field of FS theory. In addition, the types of CPFRs are also discussed to make the paper more fruitful. Today's complex network architecture faces the ever-changing threats. The cyber-attackers are always trying to discover, catch, and exploit the weaknesses in the networks. So, the security measures are essential to avoid and dismantle such threats. The CPFR has a vast structure composed of levels of membership, abstinence, and nonmembership which models uncertainty better than any other structures in the theory. Moreover, a CPFR has the ability to cope with multivariable problems. Therefore, this article proposes modeling techniques based on the complex picture fuzzy information which are used to study the effectiveness and ineffectiveness of different network securities against several threats and cyber-attack practices. Moreover, the strength and preeminence of the proposed methods are verified by studying their comparison with the existing methods.
在我们的生活中，我们无法避免不确定性。随机性、粗略的知识和模糊性导致了不确定性。在数学中，模糊集 (FS) 理论和逻辑用于对不确定事件进行建模。本文在 FS 理论领域定义了一个新的复杂图像模糊关系 (CPFR) 概念。此外，还讨论了 CPFR 的类型，以使本文更有成果。当今的复杂网络架构面临着瞬息万变的威胁。网络攻击者总是试图发现、抓住和利用网络中的弱点。因此，安全措施对于避免和消除这些威胁至关重要。CPFR 由隶属度、弃权和非隶属度的层次结构组成，它比该理论中的任何其他结构都能更好地建模不确定性。此外，CPFR 具有处理多变量问题的能力。因此，本文提出了基于复杂图像模糊信息的建模技术，用于研究不同网络安全措施对几种威胁和网络攻击实践的有效性和无效性。此外，通过研究与现有方法的比较，验证了所提出方法的优势。
