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Along with the resistance against quantum computers, isogeny-based cryptography offers attractive cryptosystems due to small key sizes and compatibility with the current elliptic curve primitives. While the state-of-the-art implementation uses Montgomery curves, which facilitates efficient elliptic curve arithmetic and isogeny computations, other forms of elliptic curves can be used to produce an efficient result. In this paper, we present the new hybrid method for isogeny-based cryptosystem using Edwards curves. Unlike the previous hybrid methods, we exploit Edwards curves for recovering the curve coefficients and Montgomery curves for other operations. To this end, we first carefully examine and compare the computational cost of Montgomery and Edwards isogenies. Then, we fine-tune and tailor Edwards isogenies in order to blend with Montgomery isogenies efficiently. Additionally, we present the implementation results of Supersingular Isogeny Diffie–Hellman (SIDH) key exchange using the proposed method. We demonstrate that our method outperforms the previously proposed hybrid method, and is as fast as Montgomery-only implementation. Our results show that proper use of Edwards curves for isogeny-based cryptosystem can be quite practical.

In this article, we propose single-trace side-channel attacks against lattice-based key encapsulation mechanisms (KEMs) that are the third-round candidates of the national institute of standards and technology (NIST) standardization project. Specifically, we analyze the message encoding operation in the encapsulation phase of lattice-based KEMs to obtain an ephemeral session key. We conclude that a single-trace leakage implies a whole key recovery: the experimental results realized on a ChipWhisperer UFO STM32F3 target board achieve a success rate of 100% for $\mathsf {CRYSTALS-KYBER}$ and $\mathsf {SABER}$ regardless of an optimization level and those greater than 79% for $\mathsf {FrodoKEM}$ . We further demonstrate that the proposed attack methodologies are not restricted to the above algorithms but are widely applicable to other NIST post-quantum cryptography (PQC) candidates, including $\mathsf {NTRU Prime}$ and $\mathsf {NTRU}$ .

In this study, the authors propose a robust measurement validation algorithm for radar target tracking in a heavy-clutter environment. This algorithm defines the prior information with the radar measurement information from the estimated target trajectory. An additional validation gate is set up within the conventional validation gate using the statistics of the prior information then only a part of the validation measurement is selected to update the target state. To verify the effectiveness of the proposed algorithm, the authors designed a simulation for target tracking. The simulation results show that the proposed algorithm has a lower root mean squared position error compared to conventional algorithms in a heavy-clutter environment.

In this paper, we present a highly optimized implementation of $$\eta _T$$ pairing on 16-bit MSP430 processor. Until now, TinyPBC provided the most optimized implementation of $$\eta _T$$ pairing on sensor platforms. Although it is well optimized for finite field arithmetic, it is not optimized at an extension field arithmetic level. Moreover, since TinyPBC requires considerable amount of memory consumption, its usability is limited on a memory-constrained sensor platforms. We have focused on optimizing not only field arithmetic level but also extension field arithmetic level. In comparison with TinyPBC, the field reduction performance could be improved about 29.1% by our proposed method. We achieved 12.22% of performance improvement for extension field sparse multiplication. Our $$\eta _T$$ pairing implementation on MSP430 computes single pairing in 1.22 s, and this result is 5.88% faster than TinyPBC. Furthermore, it requires 19.2% less memory than TinyPBC.

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and 16 th -order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a twolayer convolutional neural network (CNN). The proposed CNN is composed of six (6x6) convolution filters at the first and second layers, with (5x5) max pooling for the first layer and (2x2) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the 16 th -order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultrawideband (UWB) sensor.

We propose a stacking method for ensemble learning to distinguish micro-Doppler signals generated by human walking from background noises using radar sensors. We collected micro-Doppler signals caused by four types of background noise (line of sight (LoS), fan, snow and rain) and additionally considered micro-Doppler signals caused by human walking combined with these four types of background noise. We firstly verified the effectiveness of a fully connected deep neural network (DNN) to classify 8 types of signals. The average accuracy was 88.79% for the test set. Then, we propose a stacking method to combine two base classifiers of different structures. The average accuracy of the stacking method on the test set was 91.43%. Lastly, we designed a modified stacking method to reuse feature information stored at the previous stage and the average test accuracy increased to 95.62%. This result shows that the proposed stacking methods can be an effective approach to improve classifier&rsquo, s accuracy in recognizing human walking using micro-Doppler signals with background noise.

In this paper, we discuss the application of the particle filter for the track-before-detect to detect the target in a low SNR situation. We investigate how to find a target in the range-doppler domain without using adaptive threshold level by CFAR. To do this, we propose the modified particle filter for simple calculation of particle weights. We design the radar simulator that includes full radar signal processing for algorithm verification. In spite of a low SNR situation where it is difficult to detect a target, the proposed algorithm can estimate the optimal position of a target. As the process of the particle filter is repeated, the particles converge to the optimum position considered as the estimated position of a target.

RSA 암호 시스템은 가장 널리 사용되는 공개키 암호 알고리즘 중 하나이며, RSA 암호 시스템의 안전성은 큰 수의 인수분해의 어려움에 기반을 둔다. 따라서 RSA 암호 시스템의 합성수 을 인수분해하려는 시도는 계속 진행 중에 있다. General Number Field Sieve는 현재까지 알려진 가장 빠른 인수분해 방법이고, RSA-704를 인수분해 하는데 사용된 소프트웨어인 CADO-NFS도 GNFS를 기반으로 설계되어 있다. 그러나 CADO-NFS는 다항식 선택 과정에서 입력된 변수로부터 항상 최적의 다항식을 선택하지 못하는 문제점이 있다. 본 논문에서는 CADO-NFS의 다항식 선택 단계를 분석하고 중국인의 나머지 정리와 유클리드 거리를 사용하여 다항식을 선택하는 방법을 제안한다. 제안된 방법을 이용하면 기존의 방법보다 좋은 다항식이 매번 선택되며, RSA-1024를 인수분해 하는데 적용할 수 있을 것으로 기대한다.

The purpose of this paper is to show the effectiveness of Deep neural networks (DNN) for recognizing the micro-Doppler radar signals generated by human walking and background noises. To show this, we collected various micro-Doppler signals considering the actual human walking motion and background noise characteristics. Unlike the previous researches that required a complex feature extraction process, we directly use the FFT result of the input signal as a feature vector without any additional pre-processing. This technique helps not to use heuristic approaches to get a meaningful feature vector. We designed two types of DNN classifier. The first is the binary classifier to classify human walking Doppler signals and background noises. The second is the multiclass classifier that is roughly able to recognize a circumstance of a place as well as human walking Doppler signals. DNN for the binary classifier showed about 97.5% classification accuracy for the test dataset and DNN(ReLU) for the multiclass classifier showed about 95.6% accuracy.

In this paper, we present the first constant-time implementations of four-dimensional Gallant&ndash, Lambert&ndash, Vanstone and Galbraith&ndash, Lin&ndash, Scott (GLV-GLS) scalar multiplication using curve Ted 127 - glv 4 on 8-bit AVR, 16-bit MSP430, and 32-bit ARM processors. In Asiacrypt 2012, Longa and Sica introduced the four-dimensional GLV-GLS scalar multiplication, and they reported the implementation results on Intel processors. However, they did not consider efficient implementations on resource-constrained embedded devices. We have optimized the performance of scalar multiplication using curve Ted 127 - glv 4 on 8-bit AVR, 16-bit MSP430, and 32-bit ARM processors. Our implementations compute a variable-base scalar multiplication in 6,856,026, 4,158,453, and 447,836 cycles on AVR, MSP430, and ARM Cortex-M4 processors, respectively. Recently, Four Q -based scalar multiplication has provided the fastest implementation results on AVR, MSP430, and ARM Cortex-M4 processors to date. Compared to Four Q -based scalar multiplication, the proposed implementations require 4.49% more computational cost on AVR, but save 2.85% and 4.61% cycles on MSP430 and ARM, respectively. Our 16-bit and 32-bit implementation results set new speed records for variable-base scalar multiplication.

The purpose of this paper is to show the effectiveness of Ensemble Kalman filter (EnKF) for Pulsed Doppler radar under Track-Before-Detect (TBD) environments (Heavy Clutter Environments). To do this, we designed the Pulsed Doppler radar simulator for tracking a virtual target. This simulator includes the whole process of radar signal processing. In order to consider the nonlinearity of measurement data and TBD environment, we lowered the CFAR threshold to generate many false alarms and we consider a complicated moving path of a target. Under these conditions, the tracking performances of EnKF, Extended Kalman Filter, and Particle Filter (PF) are compared and analyzed. In this given scenario, the performances of EnKF and PF were more reliable than EKF. EnKF and PF showed similar performances. Considering complexity and diversity loss by resampling that PF requires, this result shows that EnKF can be an effective alternative of PF, because the optimization of EnKF is simpler than PF.

The isogeny-based cryptosystem is the most recent category in the field of postquantum cryptography. However, it is widely studied due to short key sizes and compatibility with the current elliptic curve primitives. The main building blocks when implementing the isogeny-based cryptosystem are isogeny computations and point operations. From isogeny construction perspective, since the cryptosystem moves along the isogeny graph, isogeny formula cannot be optimized for specific coefficients of elliptic curves. Therefore, Montgomery curves are used in the literature, due to the efficient point operation on an arbitrary elliptic curve. In this paper, we propose formulas for computing 3 and 4 isogenies on twisted Edwards curves. Additionally, we further optimize our isogeny formulas on Edwards curves and compare the computational cost of Montgomery curves. We also present the implementation results of our isogeny computations and demonstrate that isogenies on Edwards curves are as efficient as those on Montgomery curves.

In this letter, we propose a method for detecting a human subject using Doppler radar by investigating the physical characteristics of targets. Human detection has a number of applications in security, surveillance, and search-and-rescue operations. To classify a target from the Doppler signal, several features related to the physical characteristics of a target are extracted from a spectrogram. The features include the frequency of the limb motion, stride, bandwidth of the Doppler signal, and distribution of the signal strength in a spectrogram. The main contribution of this letter is the use of stride information of a target for the classification. Owing to the different lengths of legs and kinematic signatures of the target species, a human subject occupies a unique space in the domain of the stride and the frequency of limb motion. To verify the proposed method, we investigated humans, dogs, bicycles, and vehicles using the developed continuous-wave Doppler radar. The human subject is identified by a classifier of a support vector machine (SVM) trained to the extracted features. The trained SVM can detect a human subject with an accuracy of 96% with fourfold cross validation.

In this paper, we propose the human detection technique using Neural Networks to effectively classify the Doppler signals caused by human walking along with the background noise sources. The frequency or phase feature vectors converted from the given input signal are directly used as the input of Neural Networks. In addition, Gaussian noise is added in the input nodes of Neural Network in order to prevent the overfitting problem. We developed the low-cost & short-range K-band Doppler radar for the experiment. The proposed technique was examined with human walking data accompanied with the background noises caused by the fan, rain, snow, and other outdoor environmental factors. The trained Neural Network detection technique can detect human walking with 95.2% of the true positive rate and it has 4.6% of the false positive rate.

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.

The Acknowledgements section is missing in the original article. Now the Acknowledgements section is given.