Correlation Power Analysis of KASUMI and Power Resilience Analysis of Some Equivalence Classes of KASUMI S-Boxes

Gupta, Devansh; Tripathy, Somanath; Mazumdar, Bodhisatwa

doi:10.1007/s41635-020-00104-y

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…In addition, most of the current deep learning-based side channel attack models still use traditional evaluation metrics for analysis. In order to more accurately analyse the feature extraction capability of the model for power consumption, this study also introduces the Grade-Class Activation Map (Grad-CAM) [ 10 ], a weight visualisation method, combined with the Correlation Power Analysis (CPA) [ 11 ] method. The new method was used to carry out the analysis of model feature extraction capability based on heat map visualisation, and the feature extraction capability of different models was visually compared and analysed.…”

Section: Introductionmentioning

confidence: 99%

Side channel analysis based on feature fusion network

Wang²,

Tang³

et al. 2022

PLoS ONE

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Various physical information can be leaked while the encryption algorithm is running in the device. Side-channel analysis exploits these leakages to recover keys. Due to the sensitivity of deep learning to the data features, the efficiency and accuracy of side channel analysis are effectively improved with the application of deep learning algorithms. However, a considerable part of existing reserches are based on traditional neural networks. The effectiveness of key recovery is improved by increasing the size of the network. However, the computational complexity of the algorithm increases accordingly. Problems such as overfitting, low training efficiency, and low feature extraction ability also occur. In this paper, we construct an improved lightweight convolutional neural network based on the feature fusion network. The new network and the traditional neural networks are respectively applied to the side-channel analysis for comparative experiments. The results show that the new network has faster convergence, better robustness and higher accuracy. No overfitting has occurred. A heatmap visualization method was introduced for analysis. The new network has higher heat value and more concentration in the key interval. Side-channel analysis based on feature fusion network has better performance, compared with the ones based on traditional neural networks.

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Section: Introductionmentioning

confidence: 99%

Side channel analysis based on feature fusion network

Wang²,

Tang³

et al. 2022

PLoS ONE

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“…The rising number of cyber-attacks based on physical information leaks, known as side-channel analysis, gives way to a new design context [9]. In particular, power attacks are a real threat to cryptographic algorithm implementations [10,11], and it is necessary to find a balance between the intrinsic resistance of S-boxes to to those attacks and the resistance against linear and differential cryptanalysis [12]. To measure the theoretical resistance of S-boxes to differential power attacks, different metrics have been proposed, such as the order of transparency redefined and revisited under the Hamming distance leakage model [13,14], and the confusion coefficient of the variance under the Hamming weight leakage model [15].…”

Section: Introductionmentioning

confidence: 99%

Search-Space Reduction for S-Boxes Resilient to Power Attacks

et al. 2021

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The search of bijective n×n S-boxes resilient to power attacks in the space of dimension (2n)! is a controversial topic in the cryptology community nowadays. This paper proposes partitioning the space of (2n)! S-boxes into equivalence classes using the hypothetical power leakage according to the Hamming weights model, which ensures a homogeneous theoretical resistance within the class against power attacks. We developed a fast algorithm to generate these S-boxes by class. It was mathematically demonstrated that the theoretical metric confusion coefficient variance takes constant values within each class. A new search strategy—jumping over the class space—is justified to find S-boxes with high confusion coefficient variance in the space partitioned by Hamming weight classes. In addition, a decision criterion is proposed to move quickly between or within classes. The number of classes and the number of S-boxes within each class are calculated, showing that, as n increases, the class space dimension is an ever-smaller fraction of the space of S-boxes, which significantly reduces the space of search of S-boxes resilient to power attacks, when the search is performed from class to class.

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