To solve the arrearage problem that puzzled most of the mobile corporations, we propose an approach to forecast and evaluate the credits for mobile clients, devising a method that is of the coalescence of genetic algo...To solve the arrearage problem that puzzled most of the mobile corporations, we propose an approach to forecast and evaluate the credits for mobile clients, devising a method that is of the coalescence of genetic algorithm and multidimensional distinguishing model. In the end of this paper, a result of a testing application in Zhuhai Branch, GMCC was provided. The precision of the forecasting and evaluation of the client’s credit is near 90%. This study is very significant to the mobile communication corporation at all levels. The popularization of the techniques and the result would produce great benefits of both society and economy.展开更多
The Tiny Encryption Algorithm (TEA) is a Feistel block cipher well known for its simple implementation, small memory footprint, and fast execution speed. In two previous studies, genetic algorithms (GAs) were employed...The Tiny Encryption Algorithm (TEA) is a Feistel block cipher well known for its simple implementation, small memory footprint, and fast execution speed. In two previous studies, genetic algorithms (GAs) were employed to investigate the randomness of TEA output, based on which distinguishers for TEA could be designed. In this study, we used quan-tum-inspired genetic algorithms (QGAs) in the cryptanalysis of TEA. Quantum chromosomes in QGAs have the advan-tage of containing more information than the binary counterpart of the same length in GAs, and therefore generate a more diverse solution pool. We showed that QGAs could discover distinguishers for reduced cycle TEA that are more efficient than those found by classical GAs in two earlier studies. Furthermore, we applied QGAs to break four-cycle and five-cycle TEAs, a considerably harder problem, which the prior GA approach failed to solve.展开更多
低能耗轻量级分组密码(low energy lightweight block cipher,LELBC)算法是一种基于置换-替换-置换(permutation-substitution-permutation,PSP)结构的轻量级分组密码算法,主要适用于计算能力、存储空间及功耗受限的物联网终端设备,通...低能耗轻量级分组密码(low energy lightweight block cipher,LELBC)算法是一种基于置换-替换-置换(permutation-substitution-permutation,PSP)结构的轻量级分组密码算法,主要适用于计算能力、存储空间及功耗受限的物联网终端设备,通过对数据加密实现数据安全保障,因此对该算法安全性的准确评估尤为关键。为了深入研究该算法的安全性,首先建立S盒的差分-线性连通表,然后基于约束规划(constraint programming,CP)方法对S盒组件、中间层和整体结构进行数学建模,搜索得到概率为2-25.96的9轮差分-线性区分器,并进一步在这个区分器的基础上分别向前添加1轮,向后添加2轮,实现了对LELBC算法的12轮密钥恢复攻击,其中数据复杂度为228个明文,时间复杂度为2114.42次12轮加密。研究结果表明,相较于整体16轮,LELBC算法仍然具有足够轮数的安全冗余。展开更多
基金Guangdong Mobile Communication Company Limited Key Item(2001 and 2002)
文摘To solve the arrearage problem that puzzled most of the mobile corporations, we propose an approach to forecast and evaluate the credits for mobile clients, devising a method that is of the coalescence of genetic algorithm and multidimensional distinguishing model. In the end of this paper, a result of a testing application in Zhuhai Branch, GMCC was provided. The precision of the forecasting and evaluation of the client’s credit is near 90%. This study is very significant to the mobile communication corporation at all levels. The popularization of the techniques and the result would produce great benefits of both society and economy.
文摘The Tiny Encryption Algorithm (TEA) is a Feistel block cipher well known for its simple implementation, small memory footprint, and fast execution speed. In two previous studies, genetic algorithms (GAs) were employed to investigate the randomness of TEA output, based on which distinguishers for TEA could be designed. In this study, we used quan-tum-inspired genetic algorithms (QGAs) in the cryptanalysis of TEA. Quantum chromosomes in QGAs have the advan-tage of containing more information than the binary counterpart of the same length in GAs, and therefore generate a more diverse solution pool. We showed that QGAs could discover distinguishers for reduced cycle TEA that are more efficient than those found by classical GAs in two earlier studies. Furthermore, we applied QGAs to break four-cycle and five-cycle TEAs, a considerably harder problem, which the prior GA approach failed to solve.
