Ciphertext-Only Fault Analysis of the MIBS Lightweight Cryptosystem in the   Internet of Things

Li Wei; Cao Shan; Gu Dawu; Li Jiayao; Wang Menglin; Cai Tianpei; Shi Xiujin

doi:10.7544/issn1000-1239.2019.20190406

Journal of Computer Research and Development > 2019 > 56(10): 2216-2228. > DOI: 10.7544/issn1000-1239.2019.20190406 CSTR: 32373.14.issn1000-1239.2019.20190406

Li Wei, Cao Shan, Gu Dawu, Li Jiayao, Wang Menglin, Cai Tianpei, Shi Xiujin. Ciphertext-Only Fault Analysis of the MIBS Lightweight Cryptosystem in the Internet of Things[J]. Journal of Computer Research and Development, 2019, 56(10): 2216-2228. DOI: 10.7544/issn1000-1239.2019.20190406

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Ciphertext-Only Fault Analysis of the MIBS Lightweight Cryptosystem in the Internet of Things

¹(School of Computer Science and Technology, Donghua University, Shanghai 201620)
²(Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240)
³(Shanghai Key Laboratory of Scalable Computing and Systems (Shanghai Jiao Tong University), Shanghai 200240)
⁴(Shanghai Key Laboratory of Integrate Administration Technologies for Information Security (Shanghai Jiao Tong University), Shanghai 200240)

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Published Date: September 30, 2019

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Abstract

Abstract

The lightweight cryptosystem MIBS was proposed at the CANS conference in 2009. It has high efficiency in both hardware implementation and software implementation. MIBS can resist against classical cryptanalysis, such as differential analysis and linear analysis, etc. It is suitable for the resource-limited devices in the Internet of things. This paper proposes new ciphertext-only fault analysis of the MIBS cryptosystem. The attackers can apply a new fault model of Double AND and two novel distinguishers of Parzen-HW and Parzen-HW-MLE to break MIBS. The experiments only require at least 72 fault injections to recover the secret key with a success probability of no less than 99%. The method can further reduce fault injections and time, and effectively improve the attacking efficiency. It shows that the ciphertext-only fault analysis poses a serious threaten to the security of MIBS. The research also provides an important reference for the security analysis of other lightweight cryptosystems.
- lightweight cryptosystem,
- MIBS,
- ciphertext-only fault analysis,
- Internet of things,
- distinguisher

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[1]	Hu Yunshu, Zhou Jun, Cao Zhenfu, Dong Xiaolei. Lightweight Multi-User Verifiable Privacy-Preserving Gene Sequence Analysis Scheme[J]. Journal of Computer Research and Development, 2024, 61(10): 2448-2466. DOI: 10.7544/issn1000-1239.202440453
[2]	Wang Chenxu, Cheng Jiacheng, Sang Xinxin, Li Guodong, Guan Xiaohong. Data Privacy-Preserving for Blockchain: State of the Art and Trends[J]. Journal of Computer Research and Development, 2021, 58(10): 2099-2119. DOI: 10.7544/issn1000-1239.2021.20210804
[3]	Song Xiangfu, Gai Min, Zhao Shengnan, Jiang Han. Privacy-Preserving Statistics Protocol for Set-Based Computation[J]. Journal of Computer Research and Development, 2020, 57(10): 2221-2231. DOI: 10.7544/issn1000-1239.2020.20200444
[4]	Zhou Jun, Shen Huajie, Lin Zhongyun, Cao Zhenfu, Dong Xiaolei. Research Advances on Privacy Preserving in Edge Computing[J]. Journal of Computer Research and Development, 2020, 57(10): 2027-2051. DOI: 10.7544/issn1000-1239.2020.20200614
[5]	Liu Junxu, Meng Xiaofeng. Survey on Privacy-Preserving Machine Learning[J]. Journal of Computer Research and Development, 2020, 57(2): 346-362. DOI: 10.7544/issn1000-1239.2020.20190455
[6]	Song Lei, Ma Chunguang, Duan Guanghan, Yuan Qi. Privacy-Preserving Logistic Regression on Vertically Partitioned Data[J]. Journal of Computer Research and Development, 2019, 56(10): 2243-2249. DOI: 10.7544/issn1000-1239.2019.20190414
[7]	Zhou Jun, Dong Xiaolei, Cao Zhenfu. Research Advances on Privacy Preserving in Recommender Systems[J]. Journal of Computer Research and Development, 2019, 56(10): 2033-2048. DOI: 10.7544/issn1000-1239.2019.20190541
[8]	Zhu Liehuang, Gao Feng, Shen Meng, Li Yandong, Zheng Baokun, Mao Hongliang, Wu Zhen. Survey on Privacy Preserving Techniques for Blockchain Technology[J]. Journal of Computer Research and Development, 2017, 54(10): 2170-2186. DOI: 10.7544/issn1000-1239.2017.20170471
[9]	Fang Weiwei, Ren Jiang, Xia Hongke. Heterogeneous Distributed Linear Regression Privacy-Preserving Modeling[J]. Journal of Computer Research and Development, 2011, 48(9): 1685-1692.
[10]	Zhang Zhancheng, Wang Shitong, Fu-Lai Chung. Collaborative Classification Mechanism for Privacy-Preserving[J]. Journal of Computer Research and Development, 2011, 48(6): 1018-1028.