• Journal of the Economic Geographical Society of Korea
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• v.11 no.3
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• pp.492-507
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• 2008

Many classical network analysis methods approach networks in aspatial perspectives. Measuring network reliability and finding critical nodes in particular, the analyses consider only network connection topology ignoring spatial components in the network such as node attributes and edge distances. Using local network autocorrelation measure, this study handles the problem. By quantifying similarity or clustering of individual objects' attributes in space, local autocorrelation measures can indicate significance of individual nodes in a network. As an application, this study analyzed internet backbone networks in the United States using both classical disjoint product method and Getis-Ord local G statistics. In the process, two variables (population size and reliability) were applied as node attributes. The results showed that local network autocorrelation measures could provide local clusters of critical nodes enabling more empirical and realistic analysis particularly when research interests were local network ranges or impacts.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.417-430
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• 2024

As modern technology advances and the proliferation of the internet continues, cyber threats are also on the rise. To effectively counter these threats, the importance of utilizing Cyber Threat Intelligence (CTI) is becoming increasingly prominent. CTI provides information on new threats based on data from past cyber incidents, but the complexity of data and changing attack patterns present significant analytical challenges. To address these issues, this study aims to utilize graph data that can comprehensively represent multidimensional relationships. Specifically, the study constructs a heterogeneous graph based on malware data, and uses the metapath2vec node embedding technique to more effectively identify cyber attack groups. By analyzing the impact of incorporating topology information into traditional malware data, this research suggests new practical applications in the field of cyber security and contributes to overcoming the limitations of CTI analysis.