Nowadays, the homeland security field faces more difficulties in identifying suspicious or abnormal entities in huge datasets. Even though there are numerous technologies available, the objective of finding out the anomalous instances in huge semantic graphs is still a challenging point. This is because the nodes are strongly linked with innumerable links. When a node carries unique or abnormal semantics in the network, it is considered as an abnormal node. In order to understand this idea, the semantic profile of each node is generated by modeling the graph structure using various kinds of nodes and links linked to the node at a particular distance through edges. Here, the relation between the nodes is represented by a certain weight. After framing the graph structure, ternary-based feature level extraction based on assigned weight takes place. Further, the optimal feature selection from extracted rules is deployed where second-order mutual information is assigned as the fitness or the objective function. Here, the abnormal paired nodes are optimally selected using the improved Dragonfly Algorithm (DA) based on the maximum mutual information. Since the weighting factors utilized in DA are based on fitness function, the proposed algorithm is termed as Fitness-Weighed Dragonfly Algorithm (FW-DA). The effectiveness of the proposed algorithm is substantiated by comparing it with the conventional models through various performance analyses.

如今，国土安全领域在识别庞大数据集中的可疑或异常实体方面面临更多的困难。即使有许多可用的技术，在巨大的语义图中找出异常实例的目标仍然是一个挑战点。这是因为节点通过无数的链接牢固地链接在一起。当节点在网络中承载唯一或异常语义时，将其视为异常节点。为了理解该思想，通过使用各种类型的节点以及通过边缘以特定距离链接到该节点的链接对图结构进行建模，来生成每个节点的语义配置文件。在此，节点之间的关系由一定的权重表示。在对图结构进行框架化之后，将基于分配的权重进行基于三元的特征级别提取。此外，部署从提取的规则中选择最佳特征，其中将二阶互信息指定为适应度或目标函数。在此，基于最大互信息使用改进的蜻蜓算法（DA）来最佳选择异常配对节点。由于DA中使用的加权因子基于适应度函数，因此该算法被称为适应度加权蜻蜓算法（FW-DA）。通过各种性能分析将其与常规模型进行比较，从而证明了该算法的有效性。基于最大互信息，使用改进的蜻蜓算法（DA）来最佳选择异常配对节点。由于DA中使用的加权因子基于适应度函数，因此该算法被称为适应度加权蜻蜓算法（FW-DA）。通过各种性能分析将其与常规模型进行比较，从而证明了该算法的有效性。基于最大互信息，使用改进的蜻蜓算法（DA）来最佳选择异常配对节点。由于DA中使用的加权因子基于适应度函数，因此该算法被称为适应度加权蜻蜓算法（FW-DA）。通过各种性能分析将其与常规模型进行比较，从而证明了该算法的有效性。