• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Xin Wei, Sun Huiping, Chen Zhong. Analysis and Design of Distance-Bounding Protocols for RFID[J]. Journal of Computer Research and Development, 2013, 50(11): 2358-2366.
Citation: Xin Wei, Sun Huiping, Chen Zhong. Analysis and Design of Distance-Bounding Protocols for RFID[J]. Journal of Computer Research and Development, 2013, 50(11): 2358-2366.

Analysis and Design of Distance-Bounding Protocols for RFID

More Information
  • Published Date: November 14, 2013
  • Relay attacks pose a serious threat to the security of radio frequency identification systems. The adversary manipulates the communication by only relaying the verbatim messages between a reader and a tag in order to increase the communication distance between them, which breaks the implicit assumption that a tag is actually within a very short distance of a reader. The main countermeasure against relay attacks is the use of distance bounding protocols measuring the round-trip time between the reader and the tag. In 2005, Hancke and Kuhn proposed the first distance-bounding protocol dedicated to RFID system named HK. From then on, a number of relative schemes have been proposed subsequently in literature. In this paper, we design a distance-bounding protocol suited for computation limited RFID tags against relay attacks. We firstly review the existing RFID distance-bounding protocols. Weaknesses and advantages in these protocols are examined. In addition, we propose an attack model for designing and analyzing RFID distance-bounding protocols. Finally, we propose a novel distance bounding protocol based on HK protocol named HKM. It mixes the predefined challenge and the random challenge, and takes advantage of the wasting memory of HK protocol. Compared with the existing distance bounding protocols, HKM has good performance in both memory consuming and resistance to relay attacks.
  • Related Articles

    [1]Hu Jun, Chen Yan, Zhang Qinghua, Wang Guoyin. Optimal Scale Selection for Generalized Multi-Scale Set-Valued Decision Systems[J]. Journal of Computer Research and Development, 2022, 59(9): 2027-2038. DOI: 10.7544/issn1000-1239.20210196
    [2]Wang Nian, Peng Zhenghong, Cui Li. EasiFFRA: A Fast Feature Reduction Algorithm Based on Neighborhood Rough Set[J]. Journal of Computer Research and Development, 2019, 56(12): 2578-2588. DOI: 10.7544/issn1000-1239.2019.20180541
    [3]Xie Qin, Zhang Qinghua, Wang Guoyin. An Adaptive Three-way Spam Filter with Similarity Measure[J]. Journal of Computer Research and Development, 2019, 56(11): 2410-2423. DOI: 10.7544/issn1000-1239.2019.20180793
    [4]Wu Weizhi, Yang Li, Tan Anhui, Xu Youhong. Granularity Selections in Generalized Incomplete Multi-Granular Labeled Decision Systems[J]. Journal of Computer Research and Development, 2018, 55(6): 1263-1272. DOI: 10.7544/issn1000-1239.2018.20170233
    [5]Yao Sheng, Xu Feng, Zhao Peng, Ji Xia. Intuitionistic Fuzzy Entropy Feature Selection Algorithm Based on Adaptive Neighborhood Space Rough Set Model[J]. Journal of Computer Research and Development, 2018, 55(4): 802-814. DOI: 10.7544/issn1000-1239.2018.20160919
    [6]Fu Zhiyao, Gao Ling, Sun Qian, Li Yang, Gao Ni. Evaluation of Vulnerability Severity Based on Rough Sets and Attributes Reduction[J]. Journal of Computer Research and Development, 2016, 53(5): 1009-1017. DOI: 10.7544/issn1000-1239.2016.20150065
    [7]Duan Jie, Hu Qinghua, Zhang Lingjun, Qian Yuhua, Li Deyu. Feature Selection for Multi-Label Classification Based on Neighborhood Rough Sets[J]. Journal of Computer Research and Development, 2015, 52(1): 56-65. DOI: 10.7544/issn1000-1239.2015.20140544
    [8]Hu Xiaojian, Yang Shanlin, Hu Xiaoxuan, Fang Fang. Optimal Decomposition of Decision Table Systems Based on Bayesian Networks[J]. Journal of Computer Research and Development, 2007, 44(4): 667-673.
    [9]Wei Lai, Miao Duoqian, Xu Feifei, and Xia Fuchun. Research on a Covering Rough Fuzzy Set Model[J]. Journal of Computer Research and Development, 2006, 43(10): 1719-1723.
    [10]Yi Gaoxiang and Hu Heping. A Web Search Result Clustering Based on Tolerance Rough Set[J]. Journal of Computer Research and Development, 2006, 43(2): 275-280.

Catalog

    Article views (953) PDF downloads (556) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return