TY - GEN
T1 - Waves of Knowledge
T2 - 1st EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles, SmartSP 2023
AU - Amar, Michael
AU - Navanesan, Lojenaa
AU - Sayakkara, Asanka P.
AU - Oren, Yossi
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
KW - Firmware verification
KW - Malware detection
KW - Malware monitoring
KW - PLC environment
KW - Physical side-channel analysis
UR - https://www.scopus.com/pages/publications/85185727050
U2 - 10.1007/978-3-031-51630-6_11
DO - 10.1007/978-3-031-51630-6_11
M3 - Conference contribution
AN - SCOPUS:85185727050
SN - 9783031516290
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 158
EP - 170
BT - Security and Privacy in Cyber-Physical Systems and Smart Vehicles - First EAI International Conference, SmartSP 2023, Proceedings
A2 - Chen, Yu
A2 - Lin, Chung-Wei
A2 - Chen, Bo
A2 - Zhu, Qi
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 12 October 2023 through 13 October 2023
ER -