New Integral Distinguisher for Rijndael-256

The known 3-round distinguisher of Rijndael-256 is byte- oriented and 2^8 plaintexts are needed to distinguish 3-round Rijndael from a random permutation. In this paper, we consider the influence of the order of the plaintexts and present a new 3-round distinguisher which only needs 32 plaintexts

All-optical fiber multi-point photoacoustic spectroscopic gas sensing system

An all-optical fiber multi-point photoacoustic spectroscopy gas sensing system is presented. The system can operate up to ten sensing points with a 1 ppm level C2H2 detection sensitivity for each sensor

Research on Side-Channel Analysis Based on Deep Learning with Different Sample Data

With the in-depth integration of deep learning and side-channel analysis (SCA) technology, the security threats faced by embedded devices based on the Internet of Things (IoT) have become increasingly prominent. By building a neural network model as a discriminator, the correlation between the side information leaked by the cryptographic device, the key of the cryptographic algorithm, and other sensitive data can be explored. Then, the security of cryptographic products can be evaluated and analyzed. For the AES-128 cryptographic algorithm, combined with the CW308T-STM32F3 demo board on the ChipWhisperer experimental platform, a Correlation Power Analysis (CPA) is performed using the four most common deep learning methods: the multilayer perceptron (MLP), the convolutional neural network (CNN), the recurrent neural network (RNN), and the long short-term memory network (LSTM) model. The performance of each model is analyzed in turn when the samples are small data sets, sufficient data sets, and data sets of different scales. Finally, each model is comprehensively evaluated by indicators such as classifier accuracy, network loss, training time, and rank of side-channel attacks. The experimental results show that the convolutional neural network CNN classifier has higher accuracy, lower loss, better robustness, stronger generalization ability, and shorter training time. The rank value is 2, that is, only two traces can recover the correct key byte information. The comprehensive performance effect is better

Heat-balance Thermal Protection with Heat Pipes for Hypersonic Vehicle

Heat-balance thermal protection is non-ablating thermal protection for leading edge of hypersonic vehicle. Heat will be quickly transferred from high aerodynamic heating area to low aerodynamic heating area, where the energy will be released by radiation. The temperature of high aerodynamic heating area could be reduced to protect the designed structure from being burned down. Heat-balance thermal protection is summarized. The research on heat-pipe for heat-balance thermal protection is introduced

Heat-balance Thermal Protection with Heat Pipes for Hypersonic Vehicle

Heat-balance thermal protection is non-ablating thermal protection for leading edge of hypersonic vehicle. Heat will be quickly transferred from high aerodynamic heating area to low aerodynamic heating area, where the energy will be released by radiation. The temperature of high aerodynamic heating area could be reduced to protect the designed structure from being burned down. Heat-balance thermal protection is summarized. The research on heat-pipe for heat-balance thermal protection is introduced

Collision Forgery Attack on the AES-OTR Algorithm under Quantum Computing

In recent years, some general cryptographic technologies have been widely used in network platforms related to the national economy and people’s livelihood, effectively curbing network security risks and maintaining the orderly operation and normal order of society. However, due to the fast development and considerable benefits of quantum computing, the classical cryptosystem faces serious security threats, so it is crucial to analyze and assess the anti-quantum computing ability of cryptographic algorithms under the quantum security model, to enhance or perfect the design defects of related algorithms. However, the current design and research of anti-quantum cryptography primarily focus on the cryptographic structure or working mode under the quantum security model, and there is a lack of quantum security analysis on instantiated cryptographic algorithms. This paper investigates the security of AES-OTR, one of the third-round algorithms in the CAESAR competition, under the Q2 model. The periodic functions of the associated data were constructed by forging the associated data according to the parallel and serial structure characteristics of the AES-OTR algorithm in processing the associated data, and the periodic functions of the associated data were constructed multiple times based on the Simon quantum algorithm. By using the collision pair, two collision forgery attacks on the AES-OTR algorithm can be successfully implemented, and the period s is obtained by solving with a probability close to 1. The attacks in this paper caused a significant threat to the security of the AES-OTR algorithm

Research on Side-Channel Analysis Based on Deep Learning with Different Sample Data

With the in-depth integration of deep learning and side-channel analysis (SCA) technology, the security threats faced by embedded devices based on the Internet of Things (IoT) have become increasingly prominent. By building a neural network model as a discriminator, the correlation between the side information leaked by the cryptographic device, the key of the cryptographic algorithm, and other sensitive data can be explored. Then, the security of cryptographic products can be evaluated and analyzed. For the AES-128 cryptographic algorithm, combined with the CW308T-STM32F3 demo board on the ChipWhisperer experimental platform, a Correlation Power Analysis (CPA) is performed using the four most common deep learning methods: the multilayer perceptron (MLP), the convolutional neural network (CNN), the recurrent neural network (RNN), and the long short-term memory network (LSTM) model. The performance of each model is analyzed in turn when the samples are small data sets, sufficient data sets, and data sets of different scales. Finally, each model is comprehensively evaluated by indicators such as classifier accuracy, network loss, training time, and rank of side-channel attacks. The experimental results show that the convolutional neural network CNN classifier has higher accuracy, lower loss, better robustness, stronger generalization ability, and shorter training time. The rank value is 2, that is, only two traces can recover the correct key byte information. The comprehensive performance effect is better

Hollow-Core Microstructured Optical Fiber Gas Sensors

2017-2018 > Academic research: refereed > Publication in refereed journalbcrcAccepted ManuscriptRGCOthersNational Natural Science Foundation of China; The Hong Kong Polytechnic University; ITFPublishe

Ratios between circulating myeloid cells and lymphocytes are associated with mortality in severe COVID-19 patients

10.1515/med-2021-0237Open Medicine (Poland)161351-36