Machine Learning-based Industrial LAN Networks Using Honeypots

journal of Artificial Intelligence in Electrical Engineering

دوره 11، شماره 43، اسفند 2022، صفحه 33-53   

نویسندگان

Pashaei Abbasgholi¹؛ mina zolfy²

¹Department of Electrical Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran

²Department of Electrical and Computer Engineering Faculty, Tabriz University, Tabriz, Iran

چکیده

The emergence of industrial Cyberinfrastructures, the development of information communication technology in industrial fields, and the remote accessibility of automated Industrial Control Systems (ICS) lead to various cyberattacks on industrial networks and Supervisory Control and Data Acquisition (SCADA) networks. Thus, it is essential to continuously improve the security of the networks of industrial control facilities. The purpose of honeypots is to deceive the attackers so that we may learn about their tactics and behavior. Security professionals gather all pertinent data on attack methods and behavior and take decisive action to tighten security controls. The simulation results demonstrate the ML-based mechanism's efficiency in monitoring the ICS panel for detection approaches. Therefore, the designed system for early intrusion detection can protect industrial systems against vulnerabilities by alerting the shortest possible time using online data mining in the EIDS database.

کلیدواژه‌ها

Intrusion Detection System؛ Honeypot؛ Network Security؛ Machine Learning