Name
Title | ||
|---|---|---|
Side-Channel Analysis-Based Model Extraction on Intelligent CPS: An Information Theory Perspective |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00050 | 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) |
Keywords | DocType | ISBN |
Machine learning,side-channel analysis-based model extraction,information theory,intelligent CPS | Conference | 978-1-6654-1763-1 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qianqian Pan | 1 | 6 | 1.09 |
| Jun Wu | 2 | 6 | 1.07 |
| Xi Lin | 3 | 9 | 1.44 |
| Jian-hua Li | 4 | 558 | 98.16 |