ACD: An Adaptable Approach for RFID Cloning Attack Detection.

Floyd-Warshall algorithm cloning detection radio frequency identification (RFID)

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
22 Apr 2020
Historique:
received: 03 03 2020
revised: 08 04 2020
accepted: 20 04 2020
entrez: 26 4 2020
pubmed: 26 4 2020
medline: 26 4 2020
Statut: epublish

Résumé

With the rapid development of the internet of things, radio frequency identification (RFID) technology plays an important role in various fields. However, RFID systems are vulnerable to cloning attacks. This is the fabrication of one or more replicas of a genuine tag, which behave exactly as a genuine tag and fool the reader to gain legal authorization, leading to potential financial loss or reputation damage. Many advanced solutions have been proposed to combat cloning attacks, but they require extra hardware resources, or they cannot detect a clone tag in time. In this article, we make a fresh attempt to counterattack tag cloning based on spatiotemporal collisions. We propose adaptable clone detection (ACD), which can intuitively and accurately display the positions of abnormal tags in real time. It uses commercial off-the-shelf (COTS) RFID devices without extra hardware resources. We evaluate its performance in practice, and the results confirm its success at detecting cloning attacks. The average accuracy can reach 98.7%, and the recall rate can reach 96%. Extensive experiments show that it can adapt to a variety of RFID application scenarios.

Identifiants

pubmed: 32331407
pii: s20082378
doi: 10.3390/s20082378
pmc: PMC7219334
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Natural Science Foundation of China
ID : 61501458

Références

PLoS One. 2018 Mar 22;13(3):e0193951
pubmed: 29565982

Auteurs

Weiqing Huang (W)

School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China.
School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China.

Yanfang Zhang (Y)

Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China.
School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China.

Yue Feng (Y)

Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China.
School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China.

Classifications MeSH