Abstract
At present, the field of homeland security faces many obstacles while determining abnormal or suspicious entities within the huge set of data. Several approaches have been adopted from social network analysis and data mining; however, it is challenging to identify the objective of abnormal instances within the huge complicated semantic graphs. The abnormal node is the one that takes an individual or abnormal semantic in the network. Hence, for defining this notion, a graph structure is implemented for generating the semantic profile of each node by numerous kinds of nodes and links that are associated to the node in a specific distance via edges. Once the graph structure is framed, the ternary list is formed on the basis of its adjacent nodes. The abnormalities in the nodes are detected by introducing a new optimization concept referred to as biogeography optimization with fitness sorted update (BO-FBU), which is the extended version of the standard biogeography optimization algorithm (BBO). The abnormal behavior in the network is identified by the similarities among the derived rule features. Further, the performance of the proposed model is compared to the other classical models in terms of certain performance measures. These techniques will be useful to detect digital crime and forensics.
Highlights
Several approaches have been adopted from social network analysis and data mining; it is challenging to identify the objective of abnormal instances within the huge complicated semantic graphs
The abnormalities in the nodes are detected by introducing a new optimization concept referred to as biogeography optimization with fitness sorted update (BO-FBU), which is the extended version of the standard biogeography optimization algorithm (BBO)
In real-world applications, the social networks and the sensor networks are playing a crucial role from politics to healthcare and the computational analysis of graphs is a vital area of study (Wang et al, 2018) (Yao et al, 2016) (Lin & Chalupsky, 2008)
Summary
In real-world applications, the social networks and the sensor networks are playing a crucial role from politics to healthcare and the computational analysis of graphs is a vital area of study (Wang et al, 2018) (Yao et al, 2016) (Lin & Chalupsky, 2008). The ubiquitous presence of graphs includes social networks, citation networks, computer networks, biological networks, and the Web (Lhioui et al, 2017) (Etaiwi & Awajan, 2020) (Sun et al, 2020) (Rehman Javed et al, 2020) (Numan et al, 2020). The rich information these days is proliferating in real-world graphs and the attributes associated with the characteristics and properties of the information are described as the vertices and edges of the graph. “A semantic graph is a graph where nodes represent objects of different types
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