• ETRI Journal
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• v.42 no.2
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• pp.292-304
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• 2020

As side-channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side-channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning-based side-channel analysis. In particular, we outline how deep learning is applied to side-channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

• ETRI Journal
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• v.30 no.2
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• pp.315-325
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• 2008

In this paper, we first investigate the side channel analysis attack resistance of various FPGA hardware implementations of the ARIA block cipher. The analysis is performed on an FPGA test board dedicated to side channel attacks. Our results show that an unprotected implementation of ARIA allows one to recover the secret key with a low number of power or electromagnetic measurements. We also present a masking countermeasure and analyze its second-order side channel resistance by using various suitable preprocessing functions. Our experimental results clearly confirm that second-order differential side channel analysis attacks also remain a practical threat for masked hardware implementations of ARIA.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.9
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• pp.225-230
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• 2019

Recently, with the development of Internet technology, various encryption algorithms have been adopted to protect the sensing data measured by hardware devices. The Advanced Encryption Standard (AES), the most widely used encryption algorithm in the world, is also used in many devices with strong security. However, it has been found that the AES algorithm is vulnerable to side channel analysis attacks such as Differential Power Analysis (DPA) and Correlation Power Analysis (CPA). In this paper, we present a software optimization implementation technique of the AES algorithm applying the most widely known masking technique among side channel analysis attack methods.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.288-296
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• 2022

Side-channel attacks are a quiet mighty type of attack that targets specific physical implementations vulnerabilities. Even though several researchers have examined diverse means and methods of detecting side-channel attacks, at the present time a systematic review of these approaches does not exist. The purposes of this paper are to give an extensive analysis of literature on side-channel attack detection and offer intuitiveness from past research studies. In this study, a literature survey is conducted on articles related to side-channel attack detection between 2020 and 2022 from ACM and IEEE digital libraries. From the 10 publications included in the study, it appears they target either a single type of side-channel attacks or multiple types of side-channel attacks. Therefore, a vital review of each of the two categories is provided, as well as possible prospective research in this field of study.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.221-227
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• 2014

When implementing cryptographic algorithms in mobile devices like smart cards, the security against side-channel attacks should be considered. Side-channel attacks try to find critical information from the side-channel infromation obtained from the underlying cryptographic devices' execution. Especially, the power analysis attack uses the power consumption profile of the devices as the side-channel information. This paper proposes a new gate-level countermeasure against the power analysis attack and the glitch attack and suggests how to apply the measure to securely implement AES.

• Structural Engineering and Mechanics
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• v.56 no.2
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• pp.241-256
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• 2015

Channel girder bridges that consist of a deck slab and two side beams are good choices for railway bridges and urban rail transit bridges when the vertical clearance beneath the bridge is restricted. In this study, the behavior of simply supported channel girder bridges was theoretical studied based on the theory of elasticity. The accuracy of the theoretical solutions was verified by the finite element analysis. The global bending of the channel girder and the local bending of the deck slab are two contributors to the deformations and stresses of the channel girder. Because of the shear lag effect, the maximum deflection due to the global bending could be amplified by 1.0 to 1.2 times, and the effective width of the deck slab for determining the global bending stresses can be as small as 0.7 of the actual width depending on the width-to-span ratio of the channel girder. The maximum deflection and transversal stress due to the local bending are obtained at the girder ends. For the channel girders with open section side beams, the side beam twist has a negligible effect on the deflections and stresses of the channel girder. Simplified equations were also developed for calculating the maximum deformations and stresses.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.987-995
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• 2022

Deep learning-based profiling side-channel analysis has been many proposed. Deep learning-based profiling analysis is a technique that trains the relationship between the side-channel information and the intermediate values to the neural network, then finds the secret key of the attack device using the trained neural network. Recently, cross-device profiling side channel analysis was proposed to consider the realistic deep learning-based profiling side channel analysis scenarios. However, it has a limitation in that attack performance is lowered if the profiling device and the attack device have not the same chips. In this paper, an environment in which the profiling device and the attack device have not the same chips is defined as the different-device, and a novel deep learning-based profiling side-channel analysis on different-device is proposed. Also, MCNN is used to well extract the characteristic of each data. We experimented with the six different boards to verify the attack performance of the proposed method; as a result, when the proposed method was used, the minimum number of attack traces was reduced by up to 25 times compared to without the proposed method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.457-465
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• 2015

Internet of Things(IoT) technologies should be able to communication with various embedded platforms. We will need to select an appropriate cryptographic algorithm in various embedded environments because we should consider security elements in IoT communications. Therefore the lightweight block cryptographic algorithm is essential for secure communication between these kinds of embedded platforms. However, the lightweight block cryptographic algorithm has a vulnerability which can be leaked in side channel analysis. Thus we also have to consider side channel countermeasure. In this paper, we will propose the scenario of side channel analysis and confirm the vulnerability for HIGHT algorithm which is composed of ARX structure. Additionally, we will suggest countermeasure for HIGHT against side channel analysis. Finally, we will explain how much the effectiveness can be provided through comparison between countermeasure for AES and HIGHT.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.3
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• pp.477-489
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• 2014

Based on some flaws occurred for implementing a public key cryptosystem in the embedded security device, many side-channel attacks to extract the secret private key have been tried. In spite of the fact that the cryptographic exponentiation is basically composed of a sequence of multiplications and squarings, a new Square Always exponentiation algorithm was recently presented as a countermeasure against side-channel attacks based on trading multiplications for squarings. In this paper, we propose Known Power Collision Analysis and modified Doubling attacks to break the Right-to-Left Square Always exponentiation algorithm which is known resistant to the existing side-channel attacks. And we also present a Collision-based Combined Attack which is a combinational method of fault attack and power collision analysis. Furthermore, we verify that the Square Always algorithm is vulnerable to the proposed side-channel attacks using computer simulation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.229-240
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• 2014

With increasing demands for security evaluation of side-channel resistance for crypto algorithm implementations, many equipments are developed at various research institutes. Indeed, commercial products came out for the purpose of evaluation and certification tool of security products. However, various types of security products exclusive a smart card make it difficult to implement a security evaluation system for them. In this paper, we describe implementation and characteristic of the side-channel evaluation boards of the KLA-SCARF, which is the project to develop domestic side-channel evaluation system. This report would be helpful for following researchers who intend to develop side-channel evaluation boards for other security devices.