网络与信息安全学报 (Apr 2023)
Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher
Abstract
The neural distinguisher is a new tool widely used in crypto analysis of some ciphers.For SIMON-like block ciphers, there are multiple choices for their parameters, but the reasons for designer’s selection remain unexplained.Using neural distinguishers, the security of the parameters （a,b,c） of the SIMON-like with a block size of 32 bits was researched, and good choices of parameters were given.Firstly, using the idea of affine equivalence class proposed by Kölbl et al.in CRYPTO2015, these parameters can be divided into 509 classes.And 240 classes which satisfied gcd（a-b,2）=1 were mainly researched.Then a SAT/SMT model was built to help searching differential characteristics for each equivalent class.From these models, the optimal differential characteristics of SIMON-like was obtained.Using these input differences of optimal differential characteristics, the neural distinguishers were trained for the representative of each equivalence class, and the accuracy of the distinguishers was saved.It was found that 20 optimal parameters given by Kölbl et al.cannot make the neural distinguishers the lowest accuracy.On the contrary, there were 4 parameters, whose accuracy exceeds 80%.Furthermore, the 4 parameters were bad while facing neural distinguishers.Finally, comprehensively considering the choice of Kölbl et al.and the accuracy of different neural distinguishers, three good parameters, namely （6,11,1）,（1,8,3）, and（6,7,5） were given.
