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 ...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.展开更多
基金supported by the 111 Project under Grant No.B18001the National Key Research and Development Program of China under Grant No.2018YFB2202605+1 种基金the Guangdong Science and Technology Project of China under Grant No.2019B010155002the National Natural Science Foundation of China under Grant No.61672054.
文摘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.
