Detection of Geographic Information System Security Hazards in the IoT Based on Network Security Situation Awareness

Abstract

Abstract The geographic information system (GIS) is the important system these days for navigation and directing the routes. In order to detect the security risks of the GIS efficiently and accurately and to protect the security of the GIS, a GIS security risk detection method in the Internet of Things based on network security situational awareness has been proposed. The data preprocessing method based on the operation data of the GIS is adopted. The normalized and standardized processing of the operation data of the GIS is devised to reduce the dimensional difference between the data and the geographical areas. This will facilitate the subsequent applications of the GIS to make better decisions by preserving the security. The redundant data in the GIS operation data are removed by using feature reduction algorithms through the acquisition method. The security elements based on enhanced probabilistic neural network are inferred, and the useful data samples are obtained as the security elements detection set. A rough set theory is also used to realize geographic security hidden danger identification. The experimental results show that the proposed method is efficient to maintain security and to detect the geographic security risk detection within a short span of time. The detection result accuracy is high, which meets the GIS application requirements.