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Top1. Introduction
In real-world applications, the social networks and the sensor networks are playing a crucial role from politics to healthcare and hence the computational analysis of graphs is a vital area of study (Wang et al., 2018) (Yao et al., 2016) (Lin & Chalupsky, 2008). The amount of graph data generated from diverse sources is in the exploration stage, it is a bit complex to analyze and understand the graph data. 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 hence the attributes associated with the characteristics and properties of the information are described as the vertices and edges of the graph. In the similarity graphs, several lexical matching techniques were offered to detect the similarity between the node pairs (Lampropoulos et al., 2020) (Bounhas et al., 2019) (Antonello et al., 2020) (Chen et al., 2018). Among them, the semantic similarity approaches are more attractive, such that it has gained the attention of the current researchers. “A semantic graph is a graph where nodes represent objects of different types (for example, persons, papers, organizations, etc.) and links represent binary relationships between those objects (for example, friend, citation, etc.)”. Semantic graphs (Lugowski et al., 2015) (Guesmi et al., 2016) with various types of associations are known as MRNs. Further, with an increase in the web-scale graphs and high-frequency sensor data in the MRNs, the anomaly detection is of great focus. Typically, anomaly detection refers to the problem of identifying patterns in data that do not conform to an expected behavior (Assi et al., 2019) (Zhao et al., 2018) (Ahmad et al., 2017). A node is said to be suspicious and abnormal if the corresponding network encompasses the unique or abnormal semantics (Javed et al., 2020) (Mittal et al., 2020). In order to realize the concept of abnormal node detection, a semantic profile is generated for each node by means of summarizing a graph structure surrounding it. This is based on the different types of links and nodes connected to the node within a certain distance (Vlietstra et al., 2017) (Vela et al., 2017). The abnormal nodes here are identified as the nodes with abnormal semantic profiles. In the traditional approaches, random walks and SNA were developed as unsupervised network algorithms for detecting the nodes with abnormal semantic profiles. The major drawback of this approach is, they do not consider the semantics of links. Till now, the contribution of the optimization algorithms in the field of Anomaly Detection in Semantic Graphs is in the budding stage (Mittal et al., 2020) (Javed et al., n.d.).
The major contribution of the current research is described below:
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A novel optimization algorithm referred to BO-FBU is introduced for optimizing the node pairs, thereby detecting the anomaly nodes.
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The proposed method is optimizing the solution using Semantic Graphs using Metaheuristic Algorithm that will be enhanced to detect cyber-attack (Iwendi et al., 2020) (Ch et al., 2020). It will provide an optimal solution for collecting digital evidence, through to detection and classification APT attack and Study of propagation behavior. (Gupta & Sheng, 2019)
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The proposed meta-heuristic approach doesn’t get struck into local optimal point of search.
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This approach has better exploration and exploitation rate when compare with other meta-heuristic approaches which is used for anomaly detection.
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The overall evaluation shows that the presented work is 45.9%, 66.6%, 64%, 25.9% and 2.1% better than the existing models like WOA, BBO, DA, FW-DA and T-LAU, respectively