Power Analysis in Hamming Weight Model: Attacking IoT Encryption Devices

Author(s):  
Septafiansyah Dwi Putra ◽  
Arwin Datumaya Wahyudi Sumari ◽  
Imam Asrowardi ◽  
Eko Subyantoro
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yu Zhou ◽  
Yongzhuang Wei ◽  
Hailong Zhang ◽  
Wenzheng Zhang

The concept of transparency order is introduced to measure the resistance of n , m -functions against multi-bit differential power analysis in the Hamming weight model, including the original transparency order (denoted by TO ), redefined transparency order (denoted by RTO ), and modified transparency order (denoted by MTO ). In this paper, we firstly give a relationship between MTO and RTO and show that RTO is less than or equal to MTO for any n , m -functions. We also give a tight upper bound and a tight lower bound on MTO for balanced n , m -functions. Secondly, some relationships between MTO and the maximal absolute value of the Walsh transform (or the sum-of-squares indicator, algebraic immunity, and the nonlinearity of its coordinates) for n , m -functions are obtained, respectively. Finally, we give MTO and RTO for (4,4) S-boxes which are commonly used in the design of lightweight block ciphers, respectively.


2021 ◽  
Vol 16 (1) ◽  
pp. 1-13
Author(s):  
Yu Zhou ◽  
Jianyong Hu ◽  
Xudong Miao ◽  
Yu Han ◽  
Fuzhong Zhang

Abstract The notion of the confusion coefficient is a property that attempts to characterize confusion property of cryptographic algorithms against differential power analysis. In this article, we establish a relationship between the confusion coefficient and the autocorrelation function for any Boolean function and give a tight upper bound and a tight lower bound on the confusion coefficient for any (balanced) Boolean function. We also deduce some deep relationships between the sum-of-squares of the confusion coefficient and other cryptographic indicators (the sum-of-squares indicator, hamming weight, algebraic immunity and correlation immunity), respectively. Moreover, we obtain some trade-offs among the sum-of-squares of the confusion coefficient, the signal-to-noise ratio and the redefined transparency order for a Boolean function.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaoyi Duan ◽  
Dong Chen ◽  
Xiaohong Fan ◽  
Xiuying Li ◽  
Ding Ding ◽  
...  

In the power analysis attack, when the Hamming weight model is used to describe the power consumption of the chip operation data, the result of the random forest (RF) algorithm is not ideal, so a random forest classification method based on synthetic minority oversampling technique (SMOTE) is proposed. It compensates for the problem that the random forest algorithm is affected by the data imbalance and the classification accuracy of the minority classification is low, which improves the overall classification accuracy rate. The experimental results show that when the training set data is 800, the random forest algorithm predicts the correct rate of 84%, but the classification accuracy of the minority data is 0%, and the SMOTE-based random forest algorithm improves the prediction accuracy of the same set of test data by 91%. The classification accuracy rate of a few categories has increased from 0% to 100%.


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