Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (5): 1102-1117.doi: 10.1007/s11390-021-0846-6

Special Issue: Computer Architecture and Systems

• Special Section of 2020 CCF Integrated Circuit Design and Automation Conference • Previous Articles     Next Articles

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

Yi Zhong1, Jian-Hua Feng1, Senior Member, CCF, Xiao-Xin Cui1,*, Member, CCF, IEEE, and Xiao-Le Cui2, Member, CCF        

  1. 1 Institute of Microelectronics, Peking University, Beijing 100871, China;
    2 Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518055, China
  • Received:2020-07-29 Revised:2021-08-25 Online:2021-09-30 Published:2021-09-30
  • About author:Yi Zhong received his B.S. degree in microelectronics from Peking University, Beijing, in 2018. He is currently a Ph.D. candidate in microelectronic and solid-state electronics at the Laboratory of SoC, Institute of Microelectronics, Peking University, Beijing. His current research interests include IC design on neuromorphic system and hardware security.
  • Supported by:
    This work was supported by the 111 Project under Grant No. B18001, the National Key Research and Development Program of China under Grant No. 2018YFB2202605, the Guangdong Science and Technology Project of China under Grant No. 2019B010155002, and the National Natural Science Foundation of China under Grant No. 61672054.

Hardware security remains as a major concern in the circuit design flow. 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.

Key words: hardware security; logic encryption; machine learning; neural network; naive Bayes classifier;

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