Predicting Cybersecurity Threats in Critical Infrastructure for Industry 4.0: A Proactive Approach Based on Attacker...

Area of Science:

• Cybersecurity
• Industrial Control Systems (ICS)
• Industry 4.0

Background:

• Critical Infrastructure (CI) and Industrial Control Systems (ICS), including Supervisory Control and Data Acquisition (SCADA), face significant security risks due to design vulnerabilities.
• The integration of Industry 4.0 applications with CI necessitates robust security solutions to address evolving threats.

Purpose of the Study:

• To develop a novel cybersecurity prediction technique for forecasting potential cyberattacks against CI systems.
• To enhance the security of CI systems operating within Industry 4.0 environments by considering attacker motivations.

Main Methods:

• Utilized machine learning models to predict potential cyberattacks and threats.
• Incorporated analysis of attacker motivations and specific CI characteristics to forecast attack methods.

Main Results:

• Achieved a False Positive Rate (FPR) of 66% with the trained and test datasets for the proposed cybersecurity prediction model.
• Demonstrated a proactive approach to predicting cyberattack methods based on CI and attacker motivations.

Conclusions:

• The proposed machine learning model offers a novel method for predicting cyberattacks on critical infrastructure.
• Future improvements in model accuracy are anticipated with the expansion of training datasets.