A neuro-game model for analyzing strategies in the dynamic interaction of participants of phishing attacks

Abstract

A dynamic model of countering phishing attacks is considered. Cryptocurrency exchanges (CCE) and/or their clients are considered as an example of a phishing victim. The model, unlike similar ones, is based on the assumption that the dynamics of the states of the player-victim of phishing attacks and the player-intruder (fisher) is set by means of a system of differential equations. The peculiarity of this model is that it represents a bilinear differential game of quality, for which methods for solving linear differential games are not applicable and, in addition, the absence of functional restrictions on the strategies of players (even immeasurable functions are allowed) does not allow the use of traditional approach. And their solution makes it possible to form payoff matrices, which are part of the training set for artificial neural networks (ANNs). Such a collaboration of models will make it possible to accurately build an anti-phishing strategy, minimizing the costs of both a potential victim of phishing attacks and the defense side when building a secure system of communication with CCE clients. The neuro-game approach makes it possible to predict the process of countering phishing in the context of costs for both parties using different strategies.