• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.1
• /
• pp.39-49
• /
• 2020

In this paper, we show that the Chinese standard block cipher SM4 is vulnerable to the side channel attacks and present a countermeasure to resist them. We firstly validate that the secret key of SM4 can be recovered by differential power analysis(DPA) and correlation power analysis(CPA) attacks. Therefore we analyze the vulnerable element caused by power attack and propose a first order masking-based countermeasure to defeat DPA and CPA attacks. Although the proposed countermeasure unfortunately is still vulnerable to the profiling power attacks such as deep learning-based multi layer perceptron(MLP), it can sufficiently overcome the non-profiling attacks such as DPA and CPA.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.6
• /
• pp.1045-1057
• /
• 2022

Recently, the National Institute of Standards and Technology (NIST) announced four cryptographic algorithms as a standard candidates of Post-Quantum Cryptography (PQC). In this paper, we show that private key can be exposed by a non-profiling-based power analysis attack such as Correlation Power Analysis (CPA) and Differential Deep Learning Analysis (DDLA) on CRYSTALS-KYBER algorithm, which is decided as a standard in the PKE/KEM field. As a result of experiments, it was successful in recovering the linear polynomial coefficient of the private key. Furthermore, the private key can be sufficiently recovered with a 13.0 Normalized Maximum Margin (NMM) value when Hamming Weight of intermediate values is used as a label in DDLA. In addition, these non-profiling attacks can be prevented by applying countermeasures that randomly divides the ciphertext during the decryption process and randomizes the starting point of the coefficient-wise multiplication operation.

• ETRI Journal
• /
• v.42 no.2
• /
• pp.292-304
• /
• 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
• /
• v.31 no.6
• /
• pp.1127-1136
• /
• 2021

In the field of power analysis attacks, electrical noise and misalignment of the power consumption trace are the major factors that determine the success of the attack. Therefore, several studies have been conducted to overcome this problem, and one of them is a signal processing method based on wavelet transform. Up to now, discrete wavelet transform, which can compress the trace, has been mostly used for power side-channel power analysis because continuous wavelet transform techniques increase data size and analysis time, and there is no efficient scale selection method. In this paper, we propose an efficient scale selection method optimized for power analysis attacks. Furthermore, we show that the analysis performance can be greatly improved when using the proposed method. As a result of the CPA(Correlation Power Analysis) and DDLA(Differential Deep Learning Analysis) experiments, which are non-profiling attacks, we confirmed that the proposed method is effective for noise reduction and trace alignment.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.1
• /
• pp.90-99
• /
• 2003

Java applets are downloaded from web server through internet and executed in Java Virtual Machine of clients' browser. Before execution of java applets, JVM checks bytecode program with bytecode verifier and performs runtime tests with interpreter. However, these tests will not protect against undesirable runtime behavior of java applets, such as denial of service attack, email forging attack, URL spoofing attack, and annoying sound attack. In order to protect malicious applets, a technique used in this paper is java bytecode modification. This technique is used to restrict applet behavior or insert code appropriate to profiling or other monitoring efforts. Java byte modification is divided into two general forms, class-level modification involving subclassing non-final classes and method-level modification used when control over objects from final classes or interface. This paper showed that malicious applets are controlled by java bytecode modification using proxy server. This implementation does not require any changes in the web sever, JVM or web browser.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.5C
• /
• pp.523-531
• /
• 2003

Java applets are downloaded from web server through internet and executed in Java Virtual Machine of clients'browser. Before execution of java applets, JVM checks bytecode program with bytecode verifier and performs runtime tests with interpreter. However, these tests will not protect against undesirable runtime behavior of java applets, such as denial of service attack, email forging attack, URL spoofing attack, or annoying sound attack. In order to protect malicious applets, a technique used in this paper is java bytecode modification. This technique is used to restrict applet behavior or insert code appropriate to profiling or other monitoring efforts. Java byte modification is divided into two general forms, class-level modification involving subclassing non-final classes and method-level modification used when control over objects from final classes or interface. This paper showed that malicious applets are controlled by java bytecode modification using proxy server. This implementation does not require any changes in the web sever, JVM or web browser.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.1
• /
• pp.33-43
• /
• 2023

The National Institute of Standards and Technology (NIST), which is working on the Post-Quantum Cryptography (PQC) standardization project, announced four algorithms that have been finalized for standardization. In this paper, we demonstrate through experiments that private keys can be exposed by Correlation Power Analysis (CPA) and Differential Deep Learning Analysis (DDLA) attacks on polynomial coefficient-wise multiplication algorithms that operate in the process of generating signatures using CRYSTALS-Dilithium algorithm. As a result of the experiment on ARM-Cortex-M4, we succeeded in recovering the private key coefficient using CPA or DDLA attacks. In particular, when StandardScaler preprocessing and continuous wavelet transform applied power traces were used in the DDLA attack, the minimum number of power traces required for attacks is reduced and the Normalized Maximum Margines (NMM) value increased by about 3 times. Conseqently, the proposed methods significantly improves the attack performance.

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

• The Plant Pathology Journal
• /
• v.18 no.2
• /
• pp.63-67
• /
• 2002

To understand plant-pathogen interactions, a complete set of hot pepper genes differentially expressed against pathogen attack was isolated. As an initial step, hundreds of differentially expressed cDNAS were isolated from hot pepper leaves showing non-host resistance against bacterial plant pathogens (Xanthomonas campestris pv. glycines and Pseudomonas syringae pv. syringae) using differential display reverse transcription polymerase chain reaction (DDDRT-PCR) technique. Reverse Northern and Northern blot analyses revealed that 50% of those genes were differentially expressed in pepper loaves during non-host resistance response. Among them, independent genes without redundancy were micro-arrayed for further analysis. Random EST sequence database were also generated from various CDNA libraries including pepper tissue specific libraries and leaves showing non-host hypersensitive response against X. campestris pv. glycines. As a primary stage, thousands of cDNA clones were sequenced and EST data were analyzed. These clones are being spotted on glass slide to study the expression profiling. Results of this study may further broaden knowledge on plant-pathogen interactions.