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

作     者：Yi Zhong Jian-Hua Feng Xiao-Xin Cui Xiao-Le Cui Yi Zhong;Jian-Hua Feng;Xiao-Xin Cui;Xiao-Le Cui

作者机构：Institute of MicroelectronicsPeking UniversityBeijing 100871China Key Laboratory of Integrated MicrosystemsPeking University Shenzhen Graduate SchoolShenzhen 518055China 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2021年第36卷第5期

页      面：1102-1117页

核心收录：

学科分类：0839[工学-网络空间安全] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：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 the National Natural Science Foundation of China under Grant No.61672054 

主　　题：hardware security logic encryption machine learning neural network naive Bayes classifier 

摘      要：Hardware security remains as a major concern in the circuit design *** block based encryption has been widely adopted as a simple but effective protection *** this paper,the potential threat arising from the rapidly developing field,i.e.,machine learning,is *** 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 *** 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.