Abstract
Recently, Physically Unclonable Functions (PUFs) received considerable attention in order to developing security mechanisms for applications such as Internet of Things (IoT) by exploiting the natural randomness in device-specific characteristics. This approach complements and improves the conventional security algorithms that are vulnerable to security attacks due to recent advances in computational technology and fully automated hacking systems. In this project, we propose a new authentication mechanism based on a specific implementation of PUF using metallic dendrites. Dendrites are nanomaterial devices that contain unique, complex and unclonable patterns (similar to human DNAs). We propose a method to process dendrite images. The proposed framework comprises several steps including denoising, skeletonizing, pruning and feature points extraction. The feature points are represented in terms of a tree-based weighted algorithm that converts the authentication problem to a graph matching problem. The test object is compared against a database of valid patterns using a novel algorithm to perform user identification and authentication. The proposed method demonstrates a high level of accuracy and a low computational complexity that grows linearly with the number of extracted points and database size. It also significantly reduces the required in-network storage capacity and communication rates to maintain database of users in large-scale networks.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of Computing Conference 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 863-870 |
| Number of pages | 8 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781509054435 |
| DOIs | |
| State | Published - Jan 8 2018 |
| Event | 2017 SAI Computing Conference 2017 - London, United Kingdom Duration: Jul 18 2017 → Jul 20 2017 |
Other
| Other | 2017 SAI Computing Conference 2017 |
|---|---|
| Country | United Kingdom |
| City | London |
| Period | 7/18/17 → 7/20/17 |
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Keywords
- Authentication
- Graph-Matching
- Image-Processing
- Information-Security
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Software
- Artificial Intelligence
Cite this
A graph matching algorithm for user authentication in data networks using image-based physical unclonable functions. / Valehi, Ali; Razi, Abolfazl; Cambou, Bertrand; Yu, Weijie; Kozicki, Michael.
Proceedings of Computing Conference 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 863-870.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A graph matching algorithm for user authentication in data networks using image-based physical unclonable functions
AU - Valehi, Ali
AU - Razi, Abolfazl
AU - Cambou, Bertrand
AU - Yu, Weijie
AU - Kozicki, Michael
PY - 2018/1/8
Y1 - 2018/1/8
N2 - Recently, Physically Unclonable Functions (PUFs) received considerable attention in order to developing security mechanisms for applications such as Internet of Things (IoT) by exploiting the natural randomness in device-specific characteristics. This approach complements and improves the conventional security algorithms that are vulnerable to security attacks due to recent advances in computational technology and fully automated hacking systems. In this project, we propose a new authentication mechanism based on a specific implementation of PUF using metallic dendrites. Dendrites are nanomaterial devices that contain unique, complex and unclonable patterns (similar to human DNAs). We propose a method to process dendrite images. The proposed framework comprises several steps including denoising, skeletonizing, pruning and feature points extraction. The feature points are represented in terms of a tree-based weighted algorithm that converts the authentication problem to a graph matching problem. The test object is compared against a database of valid patterns using a novel algorithm to perform user identification and authentication. The proposed method demonstrates a high level of accuracy and a low computational complexity that grows linearly with the number of extracted points and database size. It also significantly reduces the required in-network storage capacity and communication rates to maintain database of users in large-scale networks.
AB - Recently, Physically Unclonable Functions (PUFs) received considerable attention in order to developing security mechanisms for applications such as Internet of Things (IoT) by exploiting the natural randomness in device-specific characteristics. This approach complements and improves the conventional security algorithms that are vulnerable to security attacks due to recent advances in computational technology and fully automated hacking systems. In this project, we propose a new authentication mechanism based on a specific implementation of PUF using metallic dendrites. Dendrites are nanomaterial devices that contain unique, complex and unclonable patterns (similar to human DNAs). We propose a method to process dendrite images. The proposed framework comprises several steps including denoising, skeletonizing, pruning and feature points extraction. The feature points are represented in terms of a tree-based weighted algorithm that converts the authentication problem to a graph matching problem. The test object is compared against a database of valid patterns using a novel algorithm to perform user identification and authentication. The proposed method demonstrates a high level of accuracy and a low computational complexity that grows linearly with the number of extracted points and database size. It also significantly reduces the required in-network storage capacity and communication rates to maintain database of users in large-scale networks.
KW - Authentication
KW - Graph-Matching
KW - Image-Processing
KW - Information-Security
UR - http://www.scopus.com/inward/record.url?scp=85046008996&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046008996&partnerID=8YFLogxK
U2 - 10.1109/SAI.2017.8252196
DO - 10.1109/SAI.2017.8252196
M3 - Conference contribution
VL - 2018-January
SP - 863
EP - 870
BT - Proceedings of Computing Conference 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -