• Journal of computational fluids engineering
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• v.19 no.2
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• pp.17-23
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• 2014

Cavitation causes a great deal of noise, damage to components, vibrations, and a loss of efficiency in devices, such as propellers, pump impellers, nozzles, injectors, torpedoes, etc. Thus, the cavitating flow simulation is of practical importance for many engineering systems. In the present work, a two-phase flow solver based on the homogeneous mixture model has been developed. The solver employs an implicit preconditioning, dual time stepping algorithm in curvilinear coordinates. The flow characteristics around Clark-Y hydrofoil were calculated and then validated by comparing with the experimental data. The lift and drag coefficients with changes of angle of attack and cavitation number were obtained. The results show that cavity length and lift, drag coefficient increase with increasing angle of attack.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3258-3279
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• 2019

Recently, ransomware has earned itself an infamous reputation as a force to reckon with in the cybercrime landscape. However, cybercriminals are adopting other unconventional means to seamlessly attain proceeds of cybercrime with little effort. Cybercriminals are now acquiring cryptocurrencies directly from benign Internet users without the need to extort a ransom from them, as is the case with ransomware. This paper investigates advances in the cryptovirology landscape by examining the state-of-the-art cryptoviral attacks. In our approach, we perform digital autopsy on the malware's source code and execute the different malware variants in a contained sandbox to deduce static and dynamic properties respectively. We examine three cryptoviral attack structures: browser-based crypto mining, memory resident crypto mining and cryptoviral extortion. These attack structures leave a trail of digital forensics evidence when the malware interacts with the file system and generates noise in form of network traffic when communicating with the C2 servers and crypto mining pools. The digital forensics evidence, which essentially are IOCs include network artifacts such as C2 server domains, IPs and cryptographic hash values of the downloaded files apart from the malware hash values. Such evidence can be used as seed into intrusion detection systems for mitigation purposes.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.183-194
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• 2004

We propose a now watermarking scheme that can be used to embed multiple bits and also resilient to geometrical transforms such as scaling, rotation, and cropping, based on off - axis holographic watermark that allows multiple watermark recovery without original content(cover image). The holographic watermark is that Fourier transformed digital hologram is embedded into cover image in the spatial domain. The proposed method has not only increased robustness with a stronger embedding but also imprescriptibility of the watermark in the evaluation process. To compare with the convention기 scheme, the spread spectrum, we embedded and recovered maximum 1,024 bits that consist of binary number over PSNR(peak signal-to-noise ratio) 39dB. And also, we computed robustness with BER(bit error rate) corresponding the above attack

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.93-96
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• 2002

This paper presents robust and efficient scene-based video watermarking method using visual rhythm (spatio-temporal slice) in compressed domain. Scene change can be detected easily using visual rhythm and video sequences are conveniently edited at the scene boundaries. Therefore, scene-based watermark embedding Process it a natural choice. Temporal spread spectrum can be achieved by applying spread spectrum methods to visual rhythm. Additive Gaussian noise, low-pass filtering, median filtering and histogram equalization attack are simulated for all frames. Frame sub-sampling is also simulated as a typical video attack Simulation results show that proposed algorithm is robust and efficient in the presence of such kind of attacks.

• ETRI Journal
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• v.29 no.5
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• pp.619-632
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• 2007

Side channel attacks are a very serious menace to embedded devices with cryptographic applications. To counteract such attacks many randomization techniques have been proposed. One efficient technique in elliptic curve cryptosystems randomizes addition chains with binary signed digit (BSD) representations of the secret key. However, when such countermeasures have been used alone, most of them have been broken by various simple power analysis attacks. In this paper, we consider combinations which can enhance the security of countermeasures using BSD representations by adding additional countermeasures. First, we propose several ways the improved countermeasures based on BSD representations can be attacked. In an actual statistical power analysis attack, the number of samples plays an important role. Therefore, we estimate the number of samples needed in the proposed attack.

• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.71-80
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• 2015

Many researches have been studied to overcome the weak points in authentication schemes of mobile devices such as pattern-authentication that is vulnerable for smudge-attack. Since random-pattern-lock authenticates users by drawing figure of predefined-shape, it can be a method for robust security. However, the authentication performance of random-pattern-lock is influenced by input noise and individual characteristics sign pattern. We introduce an optimization method of user input direction to increase the authentication accuracy of random-pattern-lock. The method uses the likelihood of each direction given an data which is angles of line drawing by user. We adjusted recognition range for each direction and achieved the authentication rate of 95.60%.

• Journal of applied mathematics & informatics
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• v.39 no.5_6
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• pp.859-882
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• 2021

In the proposed paper, a new algorithm based on Nonlinear Feedback Shift Register (NLFSR) and modified RC4A (Rivest Cipher 4A) cipher is introduced. NLFSR is used for image pixel scrambling while modified RC4A algorithm is used for pixel substitution. NLFSR used in this algorithm is of order 27 with maximum period 2^27-1 which was found using Field Programmable Gate Arrays (FPGA), a searching method. Modified RC4A algorithm is the modification of RC4A and is modified by introducing non-linear rotation operator in the Key Scheduling Algorithm (KSA) of RC4A cipher. Analysis of occlusion attack (up to 62.5% pixels), noise (salt and pepper, Poisson) attack and key sensitivity are performed to assess the concreteness of the proposed method. Also, some statistical and security analyses are evaluated on various images of different size to empirically assess the robustness of the proposed scheme.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.5
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• pp.352-360
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• 2015

Squeal noise is a harsh, high-pitched sound that occurs when railways are running at sharp curve tracks. The cause of squeal noise is known to be the transient lateral traction force between wheel and rail. Field measurements are too difficult to control the parameters. Thus, the scaled test rig should have been made in order to investigate the generating mechanism of squeal noise. The unique feature of our test rig, HSTR(Hongik Squeal Testing Rig), is that DOFs of its wheelset are as close to as those of the real railway. The attack angle and running speed of the rail roller are controlled in real time for simulating a transient characteristic of driving curve. The environment conditions, such as given axle load, running speed, and wheel's yaw angle have been identified for generating squeal noise and the squeal noise itself has been measured. The relation between wheel creepage and creep force in lateral direction and the criteria for squeal noise have been investigated, which results has been verified by finite element method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.629-635
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• 2019

In Deep Learning models derivative is implemented by error back-propagation which enables the model to learn the error and update parameters. It can find the global (or local) optimal points of parameters even in the complex models taking advantage of a huge improvement in computing power. However, deliberately generated data points can 'fool' models and degrade the performance such as prediction accuracy. Not only these adversarial examples reduce the performance but also these examples are not easily detectable with human's eyes. In this work, we propose the method to detect adversarial datasets with random noise addition. We exploit the fact that when random noise is added, prediction accuracy of non-adversarial dataset remains almost unchanged, but that of adversarial dataset changes. We set attack methods (FGSM, Saliency Map) and noise level (0-19 with max pixel value 255) as independent variables and difference of prediction accuracy when noise was added as dependent variable in a simulation experiment. We have succeeded in extracting the threshold that separates non-adversarial and adversarial dataset. We detected the adversarial dataset using this threshold.