• 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.

• 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.

• 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.

• ETRI Journal
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• v.43 no.2
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• pp.344-356
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• 2021

CRAFT is a tweakable block cipher introduced in 2019 that aims to provide strong protection against differential fault analysis. In this paper, we show that CRAFT is vulnerable to side-channel cube attacks. We apply side-channel cube attacks to CRAFT with the Hamming weight leakage assumption. We found that the first half of the secret key can be recovered from the Hamming weight leakage after the first round. Next, using the recovered key bits, we continue our attack to recover the second half of the secret key. We show that the set of equations that are solvable varies depending on the value of the key bits. Our result shows that 99.90% of the key space can be fully recovered within a practical time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.4
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• pp.109-115
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• 2010

Differential Power Analysis (DPA) is well known as one of efficient physical side-channel attack methods using leakage power consumption traces. However, since the power traces usually include the components irrelevant to the encryption, the efficiency of the DPA attack may be degraded. To enhance the performance of DPA, we introduce a pre-processing technique which extracts the encryption-related parts from the measured power consumption signals. Experimental results show that the DPA attack with the use of the proposed pre-processing method detects correct cipher keys with much smaller number of signals compared to that of the conventional DPA attack.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.5
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• pp.107-112
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• 2020

Ultra-lightweight password CHAM is an algorithm with efficient addition, rotation and XOR operations on resource constrained devices. CHAM shows high computational performance, especially on IoT platforms. However, lightweight block encryption algorithms used on the Internet of Things may be vulnerable to side channel analysis. In this paper, we demonstrate the vulnerability to side channel attack by attempting a first power analysis attack against CHAM. In addition, a safe algorithm was proposed and implemented by applying a masking technique to safely defend the attack. This implementation implements an efficient and secure CHAM block cipher using the instruction set of an 8-bit AVR processor.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.6
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• pp.11-17
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• 2012

The HC-128 stram cipher which selected for the final eSTREAM portfolio is suitable for mobile Ad-Hoc network environments because of the ability of high-speed encryption in restricted memory space. In this paper, we analyzed the vulnerability of side channel analysis attack on HC-128 stream cipher. At the first, we explain a flaw of previous theoretical analysis result which defined the complexity of side-channel attack of HC-128 stream cipher as 'low' and then re-evaluate the security against side-channel attack by estimating the concrete complexity for recovering the secret key. As a result, HC-128 stream cipher is relatively secure against side-channel attack since recovering the secret key have $2^{65}$ computation complexity which is higher than other stream cipher's one.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.439-448
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• 2017

It is news from nowhere that post-quantum cryptography has side-channel analysis vulnerability. Side-channel analysis attack method and countermeasures for code-based McEliece cryptosystem and lattice-based NTRU cryptosystem have been investigated. Unfortunately, the investigation of the ring-LWE cryptosystem in terms of side-channel analysis is as yet insufficient. In this paper, we propose a chosen ciphertext simple power analysis attack that can be applied when ring-LWE cryptography operates on 8-bit devices. Our proposed attack can recover the key only with [$log_2q$] traces. q is a parameter related to the security level. It is used 7681 and 12289 to match the common 128 and 256-bit security levels, respectively. We identify the vulnerability through experiment that can reveal the secret key in modular add while the ring-LWE decryption performed on real 8-bit devices. We also discuss the attack that uses a similarity measurement method for two vectors to reduce attack time.