TY - GEN
T1 - Physical Intrusion Detection via Signal Reflection in Fieldbus Network
AU - Huang, Hao
AU - Meng, Long
AU - Wang, Xiangming
AU - Yang, Chenwei
AU - Mei, Famao
AU - Wu, Qinqin
AU - Ma, Tengteng
AU - Liu, Yang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - As the underlying network of wind energy con-version systems (WECS), fieldbus networks are typically pro-tected from cyber attacks through network isolation. However, attackers can bypass the isolation mechanism and physically connect malicious devices to networks to launch various attacks. Existing methods mainly focus on obtaining abnormal signals from intrusion devices to detect intrusion devices. However, these methods cannot detect inactive intrusion devices which only eavesdrop without sending any signals. In this paper, we discover signal differences caused by reflection signals and propose a physical intrusion detection method based on reflection signal extraction. By extracting the voltage characteristics of the rising and falling edges of normal transmission signals and constructing fingerprints, our method can effectively detect inactive intrusion devices. The experimental results on a real CAN testbed show that the F -score for detecting inactive intrusion devices can reach 95.2 % in various attack scenarios.
AB - As the underlying network of wind energy con-version systems (WECS), fieldbus networks are typically pro-tected from cyber attacks through network isolation. However, attackers can bypass the isolation mechanism and physically connect malicious devices to networks to launch various attacks. Existing methods mainly focus on obtaining abnormal signals from intrusion devices to detect intrusion devices. However, these methods cannot detect inactive intrusion devices which only eavesdrop without sending any signals. In this paper, we discover signal differences caused by reflection signals and propose a physical intrusion detection method based on reflection signal extraction. By extracting the voltage characteristics of the rising and falling edges of normal transmission signals and constructing fingerprints, our method can effectively detect inactive intrusion devices. The experimental results on a real CAN testbed show that the F -score for detecting inactive intrusion devices can reach 95.2 % in various attack scenarios.
KW - Fieldbus Network
KW - Physical Intrusion Detection
KW - Signal Reflection
UR - https://www.scopus.com/pages/publications/85207654978
U2 - 10.1109/ICCCWorkshops62562.2024.10693746
DO - 10.1109/ICCCWorkshops62562.2024.10693746
M3 - 会议稿件
AN - SCOPUS:85207654978
T3 - International Conference on Communications in China, ICCC Workshops 2024
SP - 54
EP - 59
BT - International Conference on Communications in China, ICCC Workshops 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2024
Y2 - 7 August 2024 through 9 August 2024
ER -