This paper investigates the vulnerability of complex networks to attacks on vertices and edges. It analyzes several existing network models and real-world networks, including scientific collaboration and Internet traffic data. The network performance is measured by the average inverse geodesic length and the size of the largest connected subgraph. Four attack strategies are tested: removals based on initial or recalculated degrees and betweenness centrality. The results show that recalculated values often lead to more damage than initial ones, indicating that network structure changes as important nodes are removed. The correlation between degree and betweenness centrality is also studied. The paper is organized into sections defining quantities, explaining attack strategies, describing networks, and presenting results. It discusses the relationship between degree and betweenness centrality, the vulnerability under vertex and edge attacks, and the robustness of different network models. The study finds that betweenness-based strategies are more effective in identifying critical nodes and edges, and that the clustering coefficient and other structural properties influence network vulnerability. The results show that scale-free networks are more sensitive to vertex attacks than random or small-world networks. The paper concludes that understanding network structure and dynamics is crucial for improving network robustness and protecting against attacks.This paper investigates the vulnerability of complex networks to attacks on vertices and edges. It analyzes several existing network models and real-world networks, including scientific collaboration and Internet traffic data. The network performance is measured by the average inverse geodesic length and the size of the largest connected subgraph. Four attack strategies are tested: removals based on initial or recalculated degrees and betweenness centrality. The results show that recalculated values often lead to more damage than initial ones, indicating that network structure changes as important nodes are removed. The correlation between degree and betweenness centrality is also studied. The paper is organized into sections defining quantities, explaining attack strategies, describing networks, and presenting results. It discusses the relationship between degree and betweenness centrality, the vulnerability under vertex and edge attacks, and the robustness of different network models. The study finds that betweenness-based strategies are more effective in identifying critical nodes and edges, and that the clustering coefficient and other structural properties influence network vulnerability. The results show that scale-free networks are more sensitive to vertex attacks than random or small-world networks. The paper concludes that understanding network structure and dynamics is crucial for improving network robustness and protecting against attacks.