• ETRI Journal
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• v.43 no.5
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• pp.941-949
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• 2021

In this study, a spatial spectrum method is proposed to cope with the pilot spoofing attack (PSA) problem by exploiting the of uplink-downlink channel reciprocity in time-division-duplex multiple-input multiple-output systems. First, the spoofing attack in the uplink stage is detected by a threshold derived from the predefined false alarm based on the estimated spatial spectrum. When the PSA occurs, the transmitter (That is Alice) can detect either one or two spatial spectrum peaks. Then, the legitimate user (That is Bob) and Eve are recognized in the downlink stage via the channel reciprocity property based on the difference between the spatial spectra if PSA occurs. This way, the presence of Eve and the direction of arrival of Eve and Bob can be identified at the transmitter end. Because noise is suppressed by a spatial spectrum, the detection performance is reliable even for low signal-noise ratios and a short training length. Consequently, Bob can use beamforming to transmit secure information during the data transmission stage. Theoretical analysis and numerical simulations are performed to evaluate the performance of the proposed scheme compared with conventional methods.

• 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.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.414-419
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• 2011

Missile am fighter aircraft have been challenged by low restoring nose-down pitching moment at high angle of attach. The consequence of weak nose-down pitching moment can be resulting in a deep stall condition. Especially, the pressure oscillation has a huge effect on noise generation, structure damage, aerodynamic performance and safety, because the flow has strong unsteadiness at high angle of attack. In this paper, the unsteady aerodynamics coefficients were analyzed at high angle of attack up to 60 degrees around two dimensional NACA0012 airfoil. The two dimensional unsteady compressible Navier-Stokes equation with a LES turbulent model was calculated by OHOC (Optimized High-Order Compact) scheme. The flow conditions are Mach number of 0.3 and Reynolds number of $10^5$. The lift, drag, pressure distribution, etc. are analyzed according to the angle of attack. The results at a low angle of attack are compared with other results before a stall condition. From a certain high angle of attack, the strong vortex formed by the leading edge are flowing downstream as like Karman vortex around a circular cylinder. Unsteady velocity field, periodic vortex shedding, the unsteady pressure distribution on the airfoil surface, and the acoustic fields are analyzed. The effects of these unsteady characteristics in the aerodynamic coefficients are analyzed.

• Journal of computational fluids engineering
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• v.18 no.1
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• pp.1-6
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• 2013

Missile and fighter aircraft have been challenged by low restoring nose-down pitching moment at high angle of attach. The consequence of weak nose-down pitching moment can be resulting in a deep stall condition. Especially, the pressure oscillation has a huge effect on noise generation, structure damage, aerodynamic performance and safety, because the flow has strong unsteadiness at high angle of attack. In this paper, the unsteady aerodynamics coefficients were analyzed at high angle of attack up to 50 degrees around two dimensional NACA0012 airfoil. The two dimensional unsteady compressible Navier-Stokes equation with a LES turbulent model was calculated by OHOC (Optimized High-Order Compact) scheme. The flow conditions are Mach number of 0.3 and Reynolds number of $10^5$. The lift, drag, pressure, entropy distribution, etc. are analyzed according to the angle of attack. The results of average lift coefficients are compared with other results according to the angle of attack. From a certain high angle of attack, the strong vortex formed by the leading edge are flowing downstream as like Karman vortex around a circular cylinder. The primary and secondary oscillating frequencies are analyzed by the effects of these unsteady aerodynamic characteristics.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.141-146
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• 2019

These days, there are many applications using neural networks as parts of their system. On the other hand, adversarial examples have become an important issue concerining the security of neural networks. A classifier in neural networks can be fooled and make it miss-classified by adversarial examples. There are many research to encounter adversarial examples by using denoising methods. Some of them using GAN (Generative Adversarial Network) in order to remove adversarial noise from input images. By producing an image from generator network that is close enough to the original clean image, the adversarial examples effects can be reduced. However, there is a chance when adversarial noise can survive the approximation process because it is not like a normal noise. In this chance, we propose a research that utilizes high-level representation in the classifier by combining GAN network with a trained U-Net network. This approach focuses on minimizing the loss function on high representation terms, in order to minimize the difference between the high representation level of the clean data and the approximated output of the noisy data in the training dataset. Furthermore, the generated output is checked whether it shows minimum error compared to true label or not. U-Net network is trained with true label to make sure the generated output gives minimum error in the end. At last, the remaining adversarial noise that still exist after low-level approximation can be removed with the U-Net, because of the minimization on high representation terms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5963-5980
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• 2017

Image scrambling is an important technology in information hiding, where the Arnold transformation is widely used. Several researchers have proposed the application of $H{\acute{e}}non$ map in square image scrambling, and certain improved technologies require scrambling many times to achieve a good effect without resisting chosen-plaintext attack although it can be directly applied to non-square images. This paper presents a non-square image scrambling algorithm, which can resist chosen-plaintext attack based on a chaotic two-dimensional Sine Logistic modulation map and $H{\acute{e}}non$ map (2D-SLHM). Theoretical analysis and experimental results show that the proposed algorithm has advantages in terms of key space, efficiency, scrambling degree, ability of anti-attack and robustness to noise interference.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.6
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• pp.151-158
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• 2019

FLUSH+RELOAD attack exposes the most serious security threat among cache side channel attacks due to its high resolution and low noise. This attack is exploited by a variety of malicious programs that attempt to leak sensitive information. In order to prevent such information leakage, it is necessary to detect FLUSH+RELOAD attack in real time. In this paper, we propose a novel run-time detection technique for FLUSH+RELOAD attack by utilizing PCM (Performance Counter Monitor) of processors. For this, we conducted four kinds of experiments to observe the variation of each counter value of PCM during the execution of the attack. As a result, we found that it is possible to detect the attack by exploiting three kinds of important factors. Then, we constructed a detection algorithm based on the experimental results. Our algorithm utilizes machine learning techniques including a logistic regression and ANN(Artificial Neural Network) to learn from different execution environments. Evaluation shows that the algorithm successfully detects all kinds of attacks with relatively low false rate.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.8
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• pp.776-783
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• 2011

In modern warfare, jamming for neutralizing the enemy electronic equipments is as important as destroying them by common weapon systems. Noise jamming is a base technique of EA(Electronic Attack) and it is one of the effective jamming techniques. Noise jamming is effective regardless of enemy electronic equipment receiver types. For increasing jamming efficiency using the same output power, noise jamming bandwidth has to be similar to target receiver's bandwidth. Radar jamming source like DRFM(Digital Radio Frequency Memory) requires noise bandwidth changing immediately for time sharing multiple jamming. In this paper, we developed bandwidth changable noise jamming signal for phase sampling type DRFM and do simulation using Matlab for showing the jamming signal output.

• Convergence Security Journal
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• v.24 no.2
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• pp.201-207
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• 2024

Voice is a critical element of human communication, and the development of speech recognition models is one of the significant achievements in artificial intelligence, which has recently been applied in various aspects of human life. The application of speech recognition models in the military field is also inevitable. However, before artificial intelligence models can be applied in the military, it is necessary to research their vulnerabilities. In this study, we evaluates the military applicability of the multilingual speech recognition model "Whisper" by examining its vulnerabilities to battlefield noise, white noise, and adversarial attacks. In experiments involving battlefield noise, Whisper showed significant performance degradation with an average Character Error Rate (CER) of 72.4%, indicating difficulties in military applications. In experiments with white noise, Whisper was robust to low-intensity noise but showed performance degradation under high-intensity noise. Adversarial attack experiments revealed vulnerabilities at specific epsilon values. Therefore, the Whisper model requires improvements through fine-tuning, adversarial training, and other methods.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.406-420
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• 2015

We present a secure and robust image watermarking scheme that uses combined reversible DWT-DCT-SVD transformations to increase integrity, authentication, and confidentiality. The proposed scheme uses two different kinds of watermarking images: a reversible watermark, $W_1$, which is used for verification (ensuring integrity and authentication aspects); and a second one, $W_2$, which is defined by a logo image that provides confidentiality. Our proposed scheme is shown to be robust, while its performances are evaluated with respect to the peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), normalized cross-correlation (NCC), and running time. The robustness of the scheme is also evaluated against different attacks, including a compression attack and Salt & Pepper attack.