A multifactor model of countering targeted attacks within the framework of an infinite antagonistic game scheme

Abstract

A multifactor model for countering targeted attacks
is developed. This model considers the parties' financial resources
(FRs) in an infinite antagonistic game framework. The model helps
to make rational decisions on allocating FRs for cybersecurity
under the threat of APT attacks. The proposed model allows for
determining the game's value and assessing the degree of risk when
using optimal mixed strategies for FRs. Optimal mixed strategies
refer to a combination of strategies that maximize the player's
expected payoff, taking into account the opponent's strategies. This
concept is essential for analyzing the financial aspects of countering
APT attacks. The relevance of the research is due to the growing
number of targeted attacks on critical infrastructure objects.
Computational experiments visualize the game's results, which is
helpful for cybersecurity analysts. The solution to such a problem
contributes to the analytical search for the game's meaning. It
makes it possible to find the characteristics of the degree of risk of
the players reaching the target when they apply optimal mixed
strategies. The found solution contributes to the analysis of the
situation with counteraction to the party attacking various objects,
including critical ones, with the help of APT attacks.