Fuzzy approach on modelling cyber attacks patterns on data transfer in industrial control systems

TL;DR

This paper introduces a fuzzy modeling approach to characterize cyber attack patterns on data transfer within industrial control systems, aiming to estimate attack success probabilities based on attacker profiles and system features.

Contribution

It presents a novel fuzzy inference system to model attacker profiles and assess attack success rates in industrial control systems, enhancing cybersecurity risk analysis.

Findings

Fuzzy models effectively characterize attacker profiles.

The approach estimates attack success probabilities.

Applicable to cybersecurity risk assessment.

Abstract

Cybersecurity of industrial control system is a very complex and challenging research topic, due to the integration of these systems in national critical infrastructures. The control systems are now interconnected in industrial networks and frequently to the Internet. In this context they are becoming targets of various cyber attacks conducted by malicious people such as hackers, script kiddies, industrial spies and even foreign armies and intelligence agencies. In this paper the authors propose a way to model the most frequent attacker profiles and to estimate the success rate of an attack conducted in given conditions. The authors use a fuzzy approach for generating attacker profiles based on attacker attributes such as knowledge, technical resources and motivation. The attack success rate is obtained by using another fuzzy inference system that analyzes the attacker profile and system intrinsic characteristics.