A CASE STUDY ON AI-SUPPORTED DETECTION OF SPATIAL DATA INTEGRITY ATTACKS IN CLOUD-BASED GIS ENVIRONMENTS

Abstract

The development of cloud-based GIS environments has significantly increased the availability, scalability and operational importance of spatial data. At the same time, it has led to the emergence of new challenges in the field of cybersecurity, in particular those related to the protection of the integrity of geospatial resources. Of particular importance are attacks involving unauthorized modification of object geometry, change of descriptive attributes, violation of topological relationships, falsification of location data from GPS systems and IoT devices, as well as unauthorized access to spatial data. The aim of the article is to assess, in an application-oriented case study, the possibility of using artificial intelligence methods to detect attacks violating the integrity of spatial data in cloud GIS environments. The study compares a traditional detection mechanism based on rules and signatures with an AI-supported variant at the system level, rather than providing a full benchmark of individual machine learning algorithms. Three operational indicators were used to assess effectiveness: Attack Detection Effectiveness (ADE), Spatial Data Integrity Impact Index (SDIII) and Attack Response Time (ART). The study analyzed 160 confirmed cases of spatial data integrity violations, including attribute manipulation, coordinate modification, unauthorized data export, topological violations, GPS spoofing, and IoT data manipulation. The results indicate that the AI-assisted system achieved a higher attack detection efficiency than the traditional system. The results obtained indicate that, in the analyzed case study, the use of artificial intelligence methods can significantly increase the effectiveness of protecting the integrity of geospatial data in Cloud GIS environments.