Hengyang Normal University

About: Hengyang Normal University is a education organization based out in Hengyang, China. It is known for research contribution in the topics: Graphene & Adsorption. The organization has 1087 authors who have published 1280 publications receiving 13850 citations. The organization is also known as: Hengyang Teachers' College & Héngyáng Shīfàn Xuéyuàn.

SCENERY: a lightweight block cipher based on Feistel structure

Abstract: In this paper, we propose a new lightweight block cipher called SCENERY. The main purpose of SCENERY design applies to hardware and software platforms. SCENERY is a 64-bit block cipher supporting 80-bit keys, and its data processing consists of 28 rounds. The round function of SCENERY consists of 8 4 × 4 S-boxes in parallel and a 32 × 32 binary matrix, and we can implement SCENERY with some basic logic instructions. The hardware implementation of SCENERY only requires 1438 GE based on 0.18 um CMOS technology, and the software implementation of encrypting or decrypting a block takes approximately 1516 clock cycles on 8-bit microcontrollers and 364 clock cycles on 64-bit processors. Compared with other encryption algorithms, the performance of SCENERY is well balanced for both hardware and software. By the security analyses, SCENERY can achieve enough security margin against known attacks, such as differential cryptanalysis, linear cryptanalysis, impossible differential cryptanalysis and related-key attacks.

Side-channel analysis attacks based on deep learning network

Abstract: There has been a growing interest in the side-channel analysis (SCA) field based on deep learning (DL) technology. Various DL network or model has been developed to improve the efficiency of SCA. However, few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate values. Based on the convolutional neural network and the autoencoder, this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three sub-networks. The TAPDC model detects the periodicity of power trace, relating power to the intermediate values and mining the deeper features by the multi-layer convolutional net. We implement the TAPDC model and compare it with two classical models in a fair experiment. The evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption trace. Also, Using the classifier layer, this model links power information to the probability of intermediate value. It completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack.

Multiplicity of Solutions for Perturbed Nonhomogeneous Neumann Problem through Orlicz-Sobolev Spaces

Abstract: We investigate the existence of multiple solutions for a class of nonhomogeneous Neumann problem with a perturbed term By using variational methods and three critical point theorems of B Ricceri, we establish some new sufficient conditions under which such a problem possesses three solutions in an appropriate Orlicz-Sobolev space