2019/3

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Convolutional Neural Networks for Profiled Side-channel Analysis
(Společnost pro radioelektronické inženýrství, 2019-09) Hou, Shourong; Zhou, Yujie; Liu, Hongming
Recent studies have shown that deep learning algorithms are very effective for evaluating the security of embedded systems. The deep learning technique represented by Convolutional Neural Networks (CNNs) has proven to be a promising paradigm in the profiled side-channel analysis attacks. In this paper, we first proposed a novel CNNs architecture called DeepSCA. Considering that this work may be reproduced by other researchers, we conduct all experiments on the public ASCAD dataset, which provides electromagnetic traces of a masked 128-bit AES implementation. Our work confirms that DeepSCA significantly reduces the number of side-channel traces required to perform successful attacks on highly desynchronized datasets, which even outperforms the published optimized CNNs model. Additionally, we find that DeepSCA pre-trained from the synchronous traces works well in presence of desynchronization or jittering after a slight fine-tuning.
A Modified Compressed Sensing-Based Recovery Algorithm for Wireless Sensor Networks
(Společnost pro radioelektronické inženýrství, 2019-09) Jahanshahi, Javad Afshar; Danyali, Habibollah; Helfroush, Mohammad Sadegh
In this paper, a novel compressed sensing (CS) acquisition and joint recovery of spatiotemporal correlated signals algorithm is presented for effective data collection and precise sensors data streams reconstruction in wireless sensor networks. The CS-based proposed method utilizes~an iterative re-weighted l1-minimization and a l2 regularization to increase the reconstruction accuracy with a small number of required data transmission. Moreover, we develop~an alternating direction method of multipliers based algorithm to efficiently solve the resulting optimization problem. Numerical experiments are conducted on several test signals with~a variety of sampling ratios. The experimental results verify the effectiveness of the proposed scheme in terms of reconstruction accuracy and consumption time compared with the state of the art algorithms.
Comparison of MOSFET Gate Waffle Patterns Based on Specific On-Resistance
(Společnost pro radioelektronické inženýrství, 2019-09) Vacula, Patrik; Kote, Vlastimil; Barri, Dalibor; Vacula, Milos; Husak, Miroslav; Jakovenko, Jiri; Privitera, Salvatore
This article describes waffle power MOSFET segmentation and defines its analytic models. Although waffle gate pattern is well-known architecture for effective channel scaling without requirements on process modification, no until today precise model considering segmentation of MOSFETs with waffle gate patterns, due to bulk connections, has been there proposed. Two different MOSFET topologies with gate waffle patterns have been investigated and compared with the same on-resistance of a standard MOSFET with finger gate pattern. The first one with diagonal metal interconnections allows reaching more than 40 % area reduction. The second MOSFET with the more simple orthogonal metal interconnections allows saving more than 20 % area. Moreover, new models defining conditions where segmented power MOSFETs with waffle gate patterns occupy less area than the standard MOSFET with finger gate pattern, have been introduced.
Two-dimensional Underdetermined DOA Estimation of Quasi-stationary Signals via Sparse Bayesian Learning
(Společnost pro radioelektronické inženýrství, 2019-09) Zhang, Weike; Wang, Qingping; Huang, Jingjian; Yuan, Naichang
In order to improve the direction-of-arrival (DOA) estimation performance of quasi-stationary signals (QSS) using a uniform circular array (UCA), this paper addresses novel method in the context of sparse representation framework. Based on the Khatri-Rao transform, UCA can achieve a higher number of degrees of freedom to resolve more signals than the number of sensors. Then, by exploiting the two-dimensional (2-D) joint grid of UCA, the estimations of elevation and azimuth angles can be obtained from the sparse representation perspective. Finally, an expectation-maximization iteration method is developed to estimate DOAs of QSS from a Bayesian perspective. Since SBL makes full use of the sparse structure of QSS, thus the proposed algorithm possesses higher angular resolution and better DOA estimation precision compared with existing methods. Numerical simulation demonstrate the validity of the proposed method.
The Detection Performance of the Dual-Sequence-Frequency-Hopping Signal via Stochastic Resonance Processing under Color Noise
(Společnost pro radioelektronické inženýrství, 2019-09) Liu, Guangkai; Kang, Yanmei; Quan, Houde; Sun, Huixian; Cui, Peizhang; Guo, Chao
Can the Dual-Sequence-Frequency-Hopping (DSFH) as a military emergency communication mode work under strong color noise? And is there any detection improvement of the DSFH signal via stochastic resonance (SR) processing under color noise? To deal with this problem, we analyze the physical feature of the DSFH signal. Firstly, the signal models of transmission, reception and the intermediate frequency (IF) are constructed. And the scale transaction is used to adjust the IF signal to fit the SR. Secondly, the non-markovian Langevin Equation (LE) is transformed into a markovian one by expand the 1-D LE to~a 2-D one. Thirdly, the non-autonomous Fokker-Plank Equation (FPE) is transformed into an autonomous one by assuming that the SR transition of magnetic particles is instantaneous and introducing the decision time. Therefore, the analytical periodic steady solution of the probability density function (PDF) with the parameter of the correlation time of the color noise is obtained. Finally, the detection probability, false alarm probability and Receiver Operating Characteristics (ROC) curve are obtained, under the criterion of the maximum~a posterior probability (MAP). Theoretical and simulation results show as below: 1) whether the DSFH can work under strong color noise is decided by the correlation time of the color noise; 2) when the power intensity of the color noise is constant, the smaller the correlation time with the bigger local SNR, the greater PDF difference of the SR output under two hypothesis, leading to better detection performance.

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