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SIMON系列算法自提出以来便受到了广泛关注。积分分析方面,Wang,Fu和Chu等人给出了SIMON32和SIMON48算法的积分分析,该文在已有的分析结果上,进一步考虑了更长分组的SIMON64算法的积分分析。基于Xiang等人找到的18轮积分区分器,该文先利用中间相遇技术和部分和技术给出了25轮SIMON64/128算法的积分分析,接着利用等价密钥技术进一步降低了攻击过程中需要猜测的密钥量,并给出了26轮SIMON64/128算法的积分分析。通过进一步的分析,该文发现高版本的SIMON算法具有更好抵抗积分分析的能力。
相似文献3.
《Microelectronics Journal》2015,46(4):301-309
A compact analytical single electron transistor (SET) model is proposed. This model is based on the “orthodox theory” of single electron tunneling, valid for unlimited range of drain to source voltage, valid for single or multi-gate, symmetric or asymmetric devices and takes the background charge effect into account. This model is computationally efficient in comparison with existing models. SET characteristics produced by the proposed model have been verified against Monte Carlo simulator SIMON and show good agreement. This model has been implemented in HSPICE simulator through its Verilog-A interface to enable simulation with conventional MOS devices and single electron inverter has been simulated and verified with SIMON results. At high operating temperature, the thermionic current is taken into account. 相似文献
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In this paper, we present a multi-island single-electron transistor (MISET) model based on the orthodox theory and solving the master equation. Using SIMON simulator, we investigate the electrical characteristics of single-electron transistors (SETs) based on multiple islands and show the temperature dependence of the Coulomb oscillation of the SET with one to six islands as a function of gate voltage Vg in the temperature range from T=5 to 50 K. Values of current tend to increase proportionally with temperature. For a high drain voltage, the MISET behaved as a single-island device. This is probably because the multiple islands were electrically enlarged and merged into a single island owing to the high applied drain voltage. Finally, we compare the advantages of MISET face to single-island SETs with identical dimensions of islands. 相似文献
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基于SIMON仿真软件的单电子存储器分析 总被引:1,自引:0,他引:1
介绍了目前国际上比较流行的单电子器件仿真软件SIMON的工作原理,并且以单电子环形存储器单元电路为例,利用SIMON软件对其功能和性能进行了仿真分析,同时,还仿真了温度和随机背景电荷对单电子环形存储器单元电路的影响。研究表明,单电子环形存储器单元电路利用量子点环状电路结构形式,由外接输入电压控制各岛上的电荷,能够得到存储器的"0"和"1"状态。并且该电路对温度和背景电荷极为敏感,在温度为0K和零背景电荷条件下电路能够正常工作,但是当温度和背景电荷发生微小变化,电路的输出状态将会受到破坏。 相似文献
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针对轻量级分组密码算法SIMON的安全性分析,对SIMON32/64算法抵抗立方攻击的能力和算法内部结构对密钥比特的混淆和扩散性能力进行了评估。基于SIMON类算法的密钥编排特点和轮函数结构,结合立方分析的基本思想,利用FPGA测试平台设计了一个SIMON32/64的立方攻击和密钥中比特检测算法。测试结果表明:在立方变元取6维至24维时,对于7轮SIMON32/64算法,通过立方攻击能够直接恢复47比特密钥,攻击时间复杂度约为218.08;对于8轮SIMON32/64算法,能够直接恢复39比特密钥,攻击时间复杂度约为225.00。对于10轮,11轮SIMON32/64算法,通过立方测试均能够捕获到密钥中比特。 相似文献
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轻量级分组密码的安全性分析越来越倾于向自动化和智能化的方向发展.目前基于深度学习对轻量级分组密码进行安全性分析正在成为一个全新的研究热点.针对由美国国家安全局在2013年发布的一款轻量级分组密码SIMON算法,将深度学习技术应用于SIMON32/64的安全性分析.分别采用前馈神经网络和卷积神经网络模拟多差分密码分析当中的单输入差分-多输出差分情形,设计了应用于SIMON32/64的6~9轮深度学习区分器,并比较了2种神经网络结构在不同条件下的优劣.通过对前馈神经网络和卷积神经网络的7轮深度学习区分器向前向后各扩展1轮,提出了针对9轮SIMON32/64的候选密钥筛选方法.实验结果证实:采用128个选择明文对,可以成功地将65535个候选密钥筛选在675个以内.这说明基于深度学习的差分区分器相比传统差分区分器需要更少的时间复杂度和数据复杂度. 相似文献
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Wei LI Yixin WU Dawu GU Jiayao LI Shan CAO Menglin WANG Tianpei CAI Xiangwu DING Zhiqiang LIU 《通信学报》2019,40(11):122-137
The ciphertext-only fault analysis on the SIMON cipher was proposed by injecting a random nibble fault under the random nibble fault model.After injecting faults,every faulty ciphertext could be decrypted and the statistical distribution of all intermediate states were analyzed by the attackers.On the basis of the previous distinguishers of SEI,GF,MLE,MLE-SEI,GF-SEI and GF-MLE,four novel distinguishers of GF-MAP,HW-MLE,GF-HW and HW-MAP were proposed to reduce faults.The results show that the SIMON cipher cannot resist against the ciphertext-only fault analysis.It provides an important reference for security analysis of other ciphers. 相似文献