TY - GEN
T1 - Side-Channel Analysis-Based Model Extraction on Intelligent CPS
T2 - 2021 IEEE Congress on Cybermatics: 14th IEEE International Conferences on Internet of Things, iThings 2021, 17th IEEE International Conference on Green Computing and Communications, GreenCom 2021, 2021 IEEE International Conference on Cyber Physical and Social Computing, CPSCom 2021 and 7th IEEE International Conference on Smart Data, SmartData 2021
AU - Pan, Qianqian
AU - Wu, Jun
AU - Lin, Xi
AU - Li, Jianhua
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The intelligent cyber-physical system (CPS) has been applied in various fields, covering multiple critical infras-tructures and human daily life support areas. CPS Security is a major concern and of critical importance, especially the security of the intelligent control component. Side-channel analysis (SCA) is the common threat exploiting the weaknesses in system operation to extract information of the intelligent CPS. However, existing literature lacks the systematic theo-retical analysis of the side-channel attacks on the intelligent CPS, without the ability to quantify and measure the leaked information. To address these issues, we propose the SCA-based model extraction attack on intelligent CPS. First, we design an efficient and novel SCA-based model extraction framework, including the threat model, hierarchical attack process, and the multiple micro-space parallel search enabled weight extraction algorithm. Secondly, an information theory-empowered analy-sis model for side-channel attacks on intelligent CPS is built. We propose a mutual information-based quantification method and derive the capacity of side-channel attacks on intelligent CPS, formulating the amount of information leakage through side channels. Thirdly, we develop the theoretical bounds of the leaked information over multiple attack queries based on the data processing inequality and properties of entropy. These convergence bounds provide theoretical means to estimate the amount of information leaked. Finally, experimental evaluation, including real-world experiments, demonstrates the effective-ness of the proposed SCA-based model extraction algorithm and the information theory-based analysis method in intelligent CPS.
AB - The intelligent cyber-physical system (CPS) has been applied in various fields, covering multiple critical infras-tructures and human daily life support areas. CPS Security is a major concern and of critical importance, especially the security of the intelligent control component. Side-channel analysis (SCA) is the common threat exploiting the weaknesses in system operation to extract information of the intelligent CPS. However, existing literature lacks the systematic theo-retical analysis of the side-channel attacks on the intelligent CPS, without the ability to quantify and measure the leaked information. To address these issues, we propose the SCA-based model extraction attack on intelligent CPS. First, we design an efficient and novel SCA-based model extraction framework, including the threat model, hierarchical attack process, and the multiple micro-space parallel search enabled weight extraction algorithm. Secondly, an information theory-empowered analy-sis model for side-channel attacks on intelligent CPS is built. We propose a mutual information-based quantification method and derive the capacity of side-channel attacks on intelligent CPS, formulating the amount of information leakage through side channels. Thirdly, we develop the theoretical bounds of the leaked information over multiple attack queries based on the data processing inequality and properties of entropy. These convergence bounds provide theoretical means to estimate the amount of information leaked. Finally, experimental evaluation, including real-world experiments, demonstrates the effective-ness of the proposed SCA-based model extraction algorithm and the information theory-based analysis method in intelligent CPS.
KW - Machine learning
KW - information theory
KW - intelligent CPS
KW - side-channel analysis-based model extraction
UR - http://www.scopus.com/inward/record.url?scp=85127450060&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85127450060&partnerID=8YFLogxK
U2 - 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00050
DO - 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00050
M3 - Conference contribution
AN - SCOPUS:85127450060
T3 - Proceedings - IEEE Congress on Cybermatics: 2021 IEEE International Conferences on Internet of Things, iThings 2021, IEEE Green Computing and Communications, GreenCom 2021, IEEE Cyber, Physical and Social Computing, CPSCom 2021 and IEEE Smart Data, SmartData 2021
SP - 254
EP - 261
BT - Proceedings - IEEE Congress on Cybermatics
A2 - Zheng, James
A2 - Liu, Xiao
A2 - Luan, Tom Hao
A2 - Jayaraman, Prem Prakash
A2 - Dai, Haipeng
A2 - Mitra, Karan
A2 - Qin, Kai
A2 - Ranjan, Rajiv
A2 - Wen, Sheng
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 December 2021 through 8 December 2021
ER -