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Machine Learning Aided Key-Guessing Attack Paradigm Against Logic Block Encryption

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Abstract

Hardware security remains as a major concern in the circuit design ow. Logic block based encryption has been widely adopted as a simple but effective protection method. In this paper, the potential threat arising from the rapidly developing field, i.e., machine learning, is researched. To illustrate the challenge, this work presents a standard attack paradigm, in which a three-layer neural network and a naive Bayes classifier are utilized to exemplify the key-guessing attack on logic encryption. Backed with validation results obtained from both combinational and sequential benchmarks, the presented attack scheme can specifically accelerate the decryption process of partial keys, which may serve as a new perspective to reveal the potential vulnerability for current anti-attack designs.

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Authors and Affiliations

  1. Institute of Microelectronics, Peking University, Beijing, 100871, China

    Yi Zhong, Jian-Hua Feng & Xiao-Xin Cui

  2. Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen, 518055, China

    Xiao-Le Cui

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  1. Yi Zhong
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  2. Jian-Hua Feng
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Correspondence to Xiao-Xin Cui.

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Zhong, Y., Feng, JH., Cui, XX. et al. Machine Learning Aided Key-Guessing Attack Paradigm Against Logic Block Encryption. J. Comput. Sci. Technol. 36, 1102–1117 (2021). https://doi.org/10.1007/s11390-021-0846-6

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