An activity theory model for dynamic evolution of attack graph based on improved least square genetic algorithm

Abstract

Most of the risk assessments of the attack graph are static and have a fixed assessment scenario, which limit the real-time nature of the situation assessment. This paper presents an activity theory model to analyse the contradictions in the attack behaviour. In order to assess the maximum probability path of an attacker and dynamically remain in control for the overall situation, a definition of attacker's benefit (loss/gain) value calculated by contradictory vector is proposed. The attacker's budget is applied as an unbiased amount in the least square genetic algorithm, optimises the fitness function of the genetic algorithm. Experimental results reveal that the improved least square genetic algorithm with unbiased estimator effectuate higher gains owing to the high fit degree of fitness function. With the coming evidence, the maximum probability attack paths get a more accurate and dynamic risk assessment of the situation.