Abstract:
In today’s interconnected world, Programmable Logic Controller (PLC) devices play a crucial role in controlling and automating critical processes across various sectors. This increased connectivity, however, also brings about significant security risks, including the threat of the PLC’s control flow being subverted through malicious code injected by state-level actors. This paper offers an exploration of the use of side channels for control flow monitoring. By analyzing subtle variations in system behavior, such as power consumption and electromagnetic radiation, these side channels can be effectively leveraged to infer control flow information, and thus identify potential attacks. To accomplish this, we employ the emitted signals to train a machine learning model, and evaluate our detector by simulating two different types of attacks: malicious code injection and sensitive data infiltration. Additionally, we provide a unique comparison between the power consumption and electromagnetic side channels, highlighting the primary benefits each signal type exhibits in terms of detecting and preventing attacks. The results presented in this paper can aid system manufacturers in selecting the most suitable channel for defending their system, based on the specific requirements and context of their PLC application.
