In recent years, the relevance of cybersecurity has been increasingly evident to companies and institutions, as well as to final users. Because of that, it is important to ensure the robustness of a network. With the aim of improving the security of the network, it is desirable to find out which are the most critical nodes in order to protect them from external attackers. This work tackles this problem, named the α‐separator problem, from a heuristic perspective, proposing an algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP). In particular, a novel approach for the constructive procedure is proposed, where centrality metrics derived from social network analysis are used as a greedy criterion. Furthermore, the quality of the provided solutions is improved by means of a combination method based on Path Relinking (PR). This work explores different variants of PR, also adapting the most recent one, Exterior PR, for the problem under consideration. The combination of GRASP + PR allows the algorithm to obtain high‐quality solutions within a reasonable computing time. The proposal is supported by a set of intensive computational experiments that show the quality of the proposal, comparing it with the most competitive algorithm found in the state of art.

中文翻译：

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使用贪婪随机自适应搜索过程和路径重新链接发现网络中的弱点

近年来，对于公司和机构以及最终用户来说，网络安全的重要性日益明显。因此，确保网络的健壮性很重要。为了提高网络的安全性，期望找出最关键的节点，以保护它们免受外部攻击者的侵害。这项工作解决了这个问题，命名为α从启发式角度出发，分离器问题提出了一种基于贪婪随机自适应搜索过程（GRASP）的算法。特别是，提出了一种新颖的建设性方法，其中将从社交网络分析中得出的集中度指标用作贪婪标准。此外，通过基于路径重新链接（PR）的组合方法可以提高所提供解决方案的质量。这项工作探索了PR的不同变体，还针对考虑中的问题采用了最新的外观PR。GRASP + PR的组合使算法可以在合理的计算时间内获得高质量的解决方案。该提案得到一组密集的计算实验的支持，这些实验证明了该提案的质量，