Improving Safety Awareness Campaigns Through the Use of Graph Neural Networks

Phishing is one of the main threats against companies where the main weakness against this type of threat is the worker. For this reason, it is essential that workers have a high security awareness for which it is fundamental to carry out a good safety-awareness campaign. However, as far as we are concerned, a mathematical study of the evolution of security awareness taking into account interactions with other people has not been considered. In this paper, we study how security awareness evolves through two belief-propagation models and Graph Neural Networks. Since this approach is new, the two most basic models were chosen to simulate propagation of beliefs: Sznajd model variant and Hegselmann–Krause model. On the other hand, because Graph Neural Networks are a current and very powerful tool, it was decided to use them to analyze the evolution of beliefs. We consider that with them information-awareness campaigns can be improved. As an example, we propose different awareness measures according to future beliefs and social influence.