• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.15-23
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• 2017

Template attack is a powerful side-channel analysis technique which can be performed by an attacker who has a test device that is identical to target device. Template attack is consisted of building template in profiling phase and matching the target device using template that were calculated in profiling phase. One methods to improve the success rate of template attack is to better estimate template which is consisted sample mean and sample covariance matrix of gaussian distribution in template profiling. However restriction of power trace in profiling phase led to poor template estimation. In this paper, we propose new method to select noisy power trace in profiling phase. By eliminating noisy power trace in profiling phase, we can construct more advanced mean and covariance matrix which relates to better performance in template attack. We proved that the proposed method is valid through experiments.

• Steel and Composite Structures
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• v.37 no.5
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• pp.589-603
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• 2020

This paper presents an investigation of the thermal performance of composite floor slabs with profiled steel decking exposed to fire effects from floor. A detailed finite-element model has been developed by representing the concrete slab with steel decking under of it and steel beam both steel parts protected by intumescent coating. Although this type of floor systems offers a better fire resistance, passive fire protection materials should be applied when a higher fire resistance is desired. Moreover, fire exposed side is so crucial for composite slab systems as the total fire behaviour of the floor system changes dramatically. When the fire attack from steel parts, the temperature rises rapidly resulting in a sudden decrease on the strength of the beam and decking. Herein this paper, the fire attack side is assumed from the face of the concrete floor (top of the concrete assembly). Therefore, the heat is transferred through concrete to the steel decking and reaching finally to the steel beam both protected by intumescent coating. In this work, the numerical model has been established to predict the heat transfer performance including material properties such as thermal conductivity, specific heat and dry film thickness of intumescent coating. The developed numerical model has been divided into different layers to understand the sensitivity of steel temperature to the number of layers of intumescent coating. Results show that the protected composite floors offer a higher fire resistance as the temperature of the steel section remains below 60℃ even after 60-minute Standard (ISO) fire and Fast fire exposure. Obtaining lower temperatures in steel due to the great fire performance of the concrete itself results in lesser reductions of strength and stiffness hence, lesser deflections.