• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.585-591
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• 2013

Recently, the combined attack which is a combination of side channel analysis and fault attack has been developed to extract the secret key during the cryptographic processes using a security device. Unfortunately, an attacker can find the private key of RSA cryptosystem through one time fault injection and power signal analysis. In this paper, we diagnosed SPA/FA resistant BNP(Boscher, Naciri, and Prouff) exponentiation algorithm as having threats to a similar combined attack. And we proposed a simple countermeasure to resist against this combined attack by randomizing the private key using error infective method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1071-1078
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• 2018

Recently, a family of lightweight block ciphers CHAM that has effective performance on resource-constrained devices is proposed. The CHAM uses a stateless-on-the-fly key schedule method which can reduce the key storage areas. Furthermore, the core design of CHAM is based on ARX(Addition, Rotation and XOR) operations which can enhance the computational performance. Nevertheless, we point out that the CHAM algorithm may be vulnerable to the fault injection attack which can reveal 4 round keys and derive the secret key from them. As a simulation result, the proposed fault injection attack can extract the secret key of CHAM-128/128 block cipher using about 24 correct-faulty cipher text pairs.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.1
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• pp.12-17
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• 2013

Fault currents are expressed as a combination of harmonic components and exponentially decaying DC offset components, during the occurrence of fault in power system. The DC offset components are included, when the voltage phase angle of fault inception is closer to $0^{\circ}$ or $180^{\circ}$. The digital protection relay should be detected quickly and accurately during the faults, despite of the distortions of relaying signal by these components. It is very important to implement the robust protection algorithm, that is not affected by DC offset and harmonic components, because most relaying algorithms extract the fundamental frequency component from distorted relaying signal. So, In order to high performance in relaying, advanced DC offset removal filter is required. In this paper, a new DC offset removal filter, which is no need to preset a time constant of power system and accurately estimate the DC offset components with one cycle of data, is proposed, and compared with the other filter. In order to verify performance of the filter, we used collecting the current signals after synchronous machine modeling by ATPDraw5.7p4 software. The results of simulation, the proposed DC offset removal filter do not need any prior information, the phase delay and gain error were not occurred.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.9-20
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• 1998

In this paper, a new distance relaying scheme is proposed. Artificial neural networks are applied to the distance relaying system composed of pattern recognition based. The proposed distance relaying scheme has two blocks of pattern recognition stages to estimate the fundamental frequency and to classify the fault types. In the first block, a filtering method using neural networks called a neural networks mapping filter(NMF) is presented to efficiently extract the features. And in the sec'ond block, the estimator called neural networks fault pattern estimator(NFPE) is also presented to classify the fault types by the extracted effective features obtained from NMF. Each block of these applied schemes is trained by back-propagation algorithm of multilayer perceptron and show the fast and accurate pattern recognition by ability of multilayer neural networks. The test result of this approach are obtained the good performance from the fault transient wave signals of EMTP(e1ectromagnetic transients program) in the various fault conditions of power systems.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.91-100
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• 2011

The Triple Data Encryption Algorithm (Triple DES) is an international standard of block cipher, which composed of two encryption processes and one decryption process of DES to increase security level. In this paper, we proposed a Differential Fault Analysis (DFA) attack to retrieve secret keys using reduction of last round execution for each DES process in the Triple DES by fault injections. From the simulation result for the proposed attack method, we could extract three 56-bit secret keys using exhaustive search attack for $2^{24}$ candidate keys which are refined from about 9 faulty-correct cipher text pairs. Using laser fault injection experiment, we also verified that the proposed DFA attack could be applied to a pure microprocessor ATmega 128 chip in which the Triple DES algorithm was implemented.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.709-717
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• 2012

The round reduction on block cipher is a fault injection attack in which an attacker inserts temporary errors in cryptographic devices and extracts a secret key by reducing the number of operational round. In this paper, we proposed an improved round reduction method to retrieve master keys by injecting a fault during operation of loop statement in the Triple DES. Using laser fault injection experiment, we also verified that the proposed attack could be applied to a pure microprocessor ATmega 128 chip in which the Triple DES algorithm was implemented. Compared with previous attack method which is required 9 faulty-correct cipher text pairs and some exhaustive searches, the proposed one could extract three 56-bit secret keys with just 5 faulty cipher texts.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.4
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• pp.104-111
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• 2004

In this paper, we propose a diagnosis algorithm to detect faults of induction motor using LDA First, after reducing the input dimension of a current value measured by experiment at each period using PCA method, we extract characteristic vectors for each fault using LDA Next, we analyze the driving condition of an induction motor using the Euclidean distance between a precalculated characteristic vector and an input vector. Finally, from the experiments under various noise conditions showing the properties of the LDA method, we obtained better results than the case of using the PCA method.

• Journal of IKEEE
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• v.3 no.2 s.5
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• pp.178-185
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• 1999

Geological structures such as fault and fracture patterns provide important information about preliminary exploration of mineralized areas and geological characterization. They may be recognized and interpreted from satellite images as line-like features usually referred to as lineaments. A proposed filtering method taking the sums azimuth angle into account is utilized, by which linear edges from low contrast areas where features extend parallel to the sun direction and in mountain shadow can be effectively extracted. Then, generalized Hough transform is applied to extract lineaments which correspond to fault and fracture patterns.