Proactive Detection of Attacks on APCS Accounts Based on Analysis of User Identification Graphical Attributes

The study examines an approach for proactively detecting access subject account compromise events through graphical attribute-based user digital footprint analysis for identifying APCS users on different hardware platforms. The formation of a digital footprint occurs by calculating WebGL metrics and selecting the most significant participating attributes. The authors review the WebGL principal components, talk about their derivation and pay attention to their characteristic features. In addition, an experiment is conducted aimed at solving the problem of user identification, taking into account the selected attributes of WebGL and their values for users. In the course of the experiment on different hardware platforms, the relevant components of the WebGL user footprints are calculated and user identification is performed by solving the classification problem. To solve it, the authors apply three classification methods: the k-nearest neighbors’ method (KNN), the decision tree method and the logistic regression method. A conclusion is made about the feasibility and effectiveness of using the selected classifiers based on the analysis of classification accuracy. The results of the work can be used in systems of proactive search for information system compromise events by increasing the efficiency in the formation of a digital footprint of the user. This is achieved by allocating new meaningful WebGL attributes and increasing the accuracy of the selected classifier in conjunction with other subject attributes.