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Jo, H., Lee, Y. and Shin, S. (2022) Vulcan: Automatic Extraction and Analysis of Cyber Threat Intelligence from Unstructured Text. Computers & Security, 120, Article ID: 102763.
https://doi.org/10.1016/j.cose.2022.102763

has been cited by the following article:

• TITLE: Exploring Cyber Threat Intelligence into Land Administration Systems for Enhanced Cyber Resilience

  AUTHORS: Pierre-François Blin, Trias Aditya, Purnama Budi Santosa, Christophe Claramunt

  KEYWORDS: Cyber Threat Intelligence, Common Vulnerabilities and Exposures, Geodata, Land Administration Systems, Risk Assessment, Spatial Cadastral Data

  JOURNAL NAME: Journal of Geographic Information System, Vol.17 No.1, February 12, 2025

  ABSTRACT: The current global cybersecurity landscape, characterized by the increasing scale and sophistication of cyberattacks, underscores the importance of integrating Cyber Threat Intelligence (CTI) into Land Administration Systems (LAS). LAS services involve requests and responses concerning public and private cadastral data, including credentials of parties, ownership, and spatial parcels. This study explores the integration of CTI in LAS to enhance cyber resilience, focusing on the unique vulnerabilities of LAS, such as sensitive data management and interconnection with other critical systems related to spatial data uses and changes. The approach employs a case study of a typical country-specific LAS to analyse structured vulnerabilities and their attributes to determine the degree of vulnerability of LAS through a quantitative inductive approach. The analysis results indicate significant improvements in identifying and mitigating potential threats through CTI integration, thus enhancing cyber resilience. These findings are crucial for policymakers and practitioners to develop robust cybersecurity strategies for LAS.