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

Authors: Chundong Wang; Tong Zhao; Zheli Liu

Addresses: Department of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China ' Department of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China ' Department of Computer Science and Control Engineering, Nankai University, Tianjin, China

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.

Keywords: activity theory; risk assessment; genetic algorithm; attack graph.

DOI: 10.1504/IJICS.2020.107448

International Journal of Information and Computer Security, 2020 Vol.12 No.4, pp.397 - 415

Received: 05 Jan 2018
Accepted: 23 Feb 2018
Published online: 29 May 2020 *

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