Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher
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...
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POSTS&TELECOM PRESS Co., LTD
2023-04-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023029 |
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| author | Zezhou HOU Jiongjiong REN Shaozhen CHEN |
| author_facet | Zezhou HOU Jiongjiong REN Shaozhen CHEN |
| author_sort | Zezhou HOU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4e42310a0d7946fbbc11a670dc2c80ac |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2023-04-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-4e42310a0d7946fbbc11a670dc2c80ac2025-01-15T03:16:23ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2023-04-01915416359576442Security evaluation for parameters of SIMON-like cipher based on neural network distinguisherZezhou HOUJiongjiong RENShaozhen CHENThe 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.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023029SIMON-likeaffine equivalent classneural network distinguishercyclic shift parameter |
| spellingShingle | Zezhou HOU Jiongjiong REN Shaozhen CHEN Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher 网络与信息安全学报 SIMON-like affine equivalent class neural network distinguisher cyclic shift parameter |
| title | Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher |
| title_full | Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher |
| title_fullStr | Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher |
| title_full_unstemmed | Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher |
| title_short | Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher |
| title_sort | security evaluation for parameters of simon like cipher based on neural network distinguisher |
| topic | SIMON-like affine equivalent class neural network distinguisher cyclic shift parameter |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023029 |
| work_keys_str_mv | AT zezhouhou securityevaluationforparametersofsimonlikecipherbasedonneuralnetworkdistinguisher AT jiongjiongren securityevaluationforparametersofsimonlikecipherbasedonneuralnetworkdistinguisher AT shaozhenchen securityevaluationforparametersofsimonlikecipherbasedonneuralnetworkdistinguisher |