Attacker Localization with Machine Learning in RS-485 Industrial Control Networks

Abstract

Cyber-attacks on industrial control systems (ICSs) may cause huge damage to our society and our lives. RS-485 is a backbone network for many ICSs deployed worldwide as a standard. Attack detection in the RS-485 network has been studied in the past. However, the operator still needs to identify and eliminate the attacker in the network after detected, which may require a huge downtime of the system. We propose an attacker localization framework for RS-485 networks. This framework uses (1) a current transformer for monitoring the analog signals of the communication line and (2) machine learning for detecting and localizing the attacker. We have carried out a performance evaluation on a 200-meter scale testbed and found that regression-based localization model performed the best with an averaging aggregator. It could estimate the location of the attacker with about 100% accuracy if we could obtain 6 or 10 attacker points in the training dataset. It could also estimate the location with 93%-96% accuracy with only 4 attacker training points, which would be still practically useful for finding the attacker in RS-485 network.