• 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
Li Wei, Ge Chenyu, Gu Dawu, Liao Linfeng, Gao Zhiyong, Guo Zheng, Liu Ya, Liu Zhiqiang, Shi Xiujin. Research on the LED Lightweight Cipher Against the Statistical Fault Analysis in Internet of Things[J]. Journal of Computer Research and Development, 2017, 54(10): 2205-2214. DOI: 10.7544/issn1000-1239.2017.20170437
Citation: Li Wei, Ge Chenyu, Gu Dawu, Liao Linfeng, Gao Zhiyong, Guo Zheng, Liu Ya, Liu Zhiqiang, Shi Xiujin. Research on the LED Lightweight Cipher Against the Statistical Fault Analysis in Internet of Things[J]. Journal of Computer Research and Development, 2017, 54(10): 2205-2214. DOI: 10.7544/issn1000-1239.2017.20170437

Research on the LED Lightweight Cipher Against the Statistical Fault Analysis in Internet of Things

More Information
  • Published Date: September 30, 2017
  • The typical lightweight cipher LED, proposed in CHES 2011, is applied in the Internet of things (IoT) to provide security for RFID tags and smart cards etc. Fault analysis has become an important method of cryptanalysis to evaluate the security of lightweight ciphers, depending on its fast speed, simple implementation, complex defense, etc. On the basis of the half byte-oriented fault model, we propose new statistical fault analysis on the LED cipher by inducing faults. Simulating experiment shows that our attack can recover its 64-bit and 128-bit secret keys with 99% probability using an SEI distinguisher, a GF distinguisher and a GF-SEI distinguisher, respectively. The attack can be implemented in the ciphertext-only attacking environment to improve the attacking efficiency and decrease the number of faults. It provides vital reference for security analysis of other lightweight ciphers in the Internet of things.
  • Related Articles

    [1]Cheng Haodong, Han Meng, Zhang Ni, Li Xiaojuan, Wang Le. Closed High Utility Itemsets Mining over Data Stream Based on Sliding Window Model[J]. Journal of Computer Research and Development, 2021, 58(11): 2500-2514. DOI: 10.7544/issn1000-1239.2021.20200554
    [2]Zhang Xiaojian, Wang Miao, Meng Xiaofeng. An Accurate Method for Mining top-k Frequent Pattern Under Differential Privacy[J]. Journal of Computer Research and Development, 2014, 51(1): 104-114.
    [3]Lei Xiangxin, Yang Zhiying, Huang Shaoyin, Hu Yunfa. Mining Frequent Subtree on Paging XML Data Stream[J]. Journal of Computer Research and Development, 2012, 49(9): 1926-1936.
    [4]Liao Guoqiong, Wu Lingqin, Wan Changxuan. Frequent Patterns Mining over Uncertain Data Streams Based on Probability Decay Window Model[J]. Journal of Computer Research and Development, 2012, 49(5): 1105-1115.
    [5]Zhu Ranwei, Wang Peng, and Liu Majin. Algorithm Based on Counting for Mining Frequent Items over Data Stream[J]. Journal of Computer Research and Development, 2011, 48(10): 1803-1811.
    [6]Tong Yongxin, Zhang Yuanyuan, Yuan Mei, Ma Shilong, Yu Dan, Zhao Li. An Efficient Algorithm for Mining Compressed Sequential Patterns[J]. Journal of Computer Research and Development, 2010, 47(1): 72-80.
    [7]Liu Xuejun, Xu Hongbing, Dong Yisheng, Qian Jiangbo, Wang Yongli. Mining Frequent Closed Patterns from a Sliding Window over Data Streams[J]. Journal of Computer Research and Development, 2006, 43(10): 1738-1743.
    [8]Liu Xuejun, Xu Hongbing, Dong Yisheng, Wang Yongli, Qian Jiangbo. Mining Frequent Patterns in Data Streams[J]. Journal of Computer Research and Development, 2005, 42(12): 2192-2198.
    [9]Ma Haibing, Zhang Chenghong, Zhang Jin, and Hu Yunfa. Mining Frequent Patterns Based on IS\++-Tree Model[J]. Journal of Computer Research and Development, 2005, 42(4): 588-593.
    [10]Wang Wei, Zhou Haofeng, Yuan Qingqing, Lou Yubo, and Sui Baile. Mining Frequent Patterns Based on Graph Theory[J]. Journal of Computer Research and Development, 2005, 42(2): 230-235.

Catalog

    Article views (1707) PDF downloads (652) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return