• Convergence Security Journal
• /
• v.14 no.4
• /
• pp.27-32
• /
• 2014

Utilization of web application has been widely spread and complication in recent years by the rapid development of network technologies and changes in the computing environment. The attack being target of this is increasing and the means is diverse and intelligent while these web applications are using to a lot of important services. In this paper, we proposed security model using virtualization technology to prevent attacks using vulnerabilities of web application. The request information for query in a database server also can be recognized by conveying to the virtual web server after ID is given to created session by the client request and the type of the query is analyzed in this request. VM-Master module is constructed in order to monitor traffic between the virtual web servers and prevent the waste of resources of Host OS. The performance of attack detection and resource utilization of the proposed method is experimentally confirmed.

• Wind and Structures
• /
• v.30 no.2
• /
• pp.181-198
• /
• 2020

In the traditional buffeting response analysis method, the spanwise incomplete correlation of buffeting forces is always assumed to be same as that of the incident wind turbulence and the action of the signature turbulence is ignored. In this paper, three typical bridge decks usually adopted in the real bridge engineering, a single flat box deck, a central slotted box deck and a two-separated paralleled box deck, were employed as the investigated objects. The wind induced pressure on these bridge decks were measured via a series of wind tunnel pressure tests of the sectional models. The influences of the wind speed in the tests, the angle of attack, the turbulence intensity and the characteristic distance were taken into account and discussed. The spanwise root coherence of buffeting forces was also compared with that of the incidence turbulence. The signature turbulence effect on the spanwise root coherence function was decomposed and explained by a new empirical method with a double-variable model. Finally, the formula of a sum of rational fractions that accounted for the signature turbulence effect was proposed in order to fit the results of the spanwise root coherence function. The results show that, the spanwise root coherence of the drag force agrees with that of incidence turbulence in some range of the reduced frequency but disagree in the mostly reduced frequency. The spanwise root coherence of the lift force and the torsional moment is much larger than that of the incidence turbulence. The influences of the wind speed and the angle of attack are slight, and they can be ignored in the wind tunnel test. The spanwise coherence function often involves several narrow peaks due to the signature turbulence effect in the high reduced frequency zone. The spanwise coherence function is related to the spanwise separation distance and the spanwise integral length scales, and the signature turbulence effect is related to the deck-width-related reduced frequency.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2008.04a
• /
• pp.1037-1040
• /
• 2008

In order to predict the remaining life of marine concrete structures under climatic loads, it is necessary to develop an analytical approach to predict the time and space dependent deterioration of concrete structures due to mainly chloride attack up to corrosion initiation and additional deterioration like cracking of cover concrete. This study aims to introduce FEM model for life-time simulation of concrete structures subjected to chloride attack. In order to consider uncertainties in materials as well as environmental parameters for the prediction, Monte Carlo Simulation is integrated in that FEM modeling for reliability-based remaining service life prediction. The paper is organized as follows: firstly general scheme for reliability-based remaining service life of concrete structures is introduced, then the FEM models for chloride penetration, corrosion product expansion and cover cracking are briefly explained, finally an example is demonstrated and the effects of localization of chloride concentration and corrosion product expansion on service life using above model are discussed.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.5
• /
• pp.945-956
• /
• 2020

The publication of data is essential in order to receive high quality services from many applications. However, if the original data is published as it is, there is a risk that sensitive information (political tendency, disease, ets.) may reveal. Therefore, many research have been proposed, not the original data but the synthetic data generating and publishing to privacy preserve. but, there is a risk of privacy leakage still even if simply generate and publish the synthetic data by various attacks (linkage attack, inference attack, etc.). In this paper, we propose a synthetic data generation algorithm in which privacy preserved by applying differential privacy the latest privacy protection technique to GAN, which is drawing attention as a synthetic data generative model in order to prevent the leakage of such sensitive information. The generative model used CGAN for efficient learning of labeled data, and applied Rényi differential privacy, which is relaxation of differential privacy, considering the utility aspects of the data. And validation of the utility of the generated data is conducted and compared through various classifiers.

• Journal of Internet Computing and Services
• /
• v.22 no.1
• /
• pp.13-22
• /
• 2021

Recently network based attack technologies are rapidly advanced and intelligent, the limitations of existing signature-based intrusion detection systems are becoming clear. The reason is that signature-based detection methods lack generalization capabilities for new attacks such as APT attacks. To solve these problems, research on machine learning-based intrusion detection systems is being actively conducted. However, in the actual network environment, attack samples are collected very little compared to normal samples, resulting in class imbalance problems. When a supervised learning-based anomaly detection model is trained with such data, the result is biased to the normal sample. In this paper, we propose to overcome this imbalance problem through One-Class Anomaly Detection using an auto encoder. The experiment was conducted through the NSL-KDD data set and compares the performance with the supervised learning models for the performance evaluation of the proposed method.

• Journal of Digital Convergence
• /
• v.17 no.7
• /
• pp.285-292
• /
• 2019

With the popularization of PC, SNS and IoT, a lot of data is generated and the amount is increasing exponentially. Artificial neural network learning is a topic that attracts attention in many fields in recent years by using huge amounts of data. Artificial neural network learning has shown tremendous potential in speech recognition and image recognition, and is widely applied to a variety of complex areas such as medical diagnosis, artificial intelligence games, and face recognition. The results of artificial neural networks are accurate enough to surpass real human beings. Despite these many advantages, privacy problems still exist in artificial neural network learning. Learning data for artificial neural network learning includes various information including personal sensitive information, so that privacy can be exposed due to malicious attackers. There is a privacy risk that occurs when an attacker interferes with learning and degrades learning or attacks a model that has completed learning. In this paper, we analyze the attack method of the recently proposed neural network model and its privacy protection method.

• Journal of Convergence for Information Technology
• /
• v.9 no.8
• /
• pp.35-44
• /
• 2019

The purpose of this study is to find out quantitative vulnerability assessment about COTS(Commercial Off The Shelf) O/S based I&C System. This paper analyzed vulnerability's lifecycle and it's impact. this paper is to develop a quantitative assessment of overall cyber security risks and vulnerabilities I&C System by studying the vulnerability analysis and prediction method. The probabilistic vulnerability assessment method proposed in this study suggests a modeling method that enables setting priority of patches, threshold setting of vulnerable size, and attack path in a commercial OS-based measurement control system that is difficult to patch an immediate vulnerability.

• The Journal of Korean Institute of Information Technology
• /
• v.17 no.12
• /
• pp.125-134
• /
• 2019

As the limitations of the passive recognition domain, which is not guaranteed transparency of the operation process, AI technology has a vulnerability that depends on the data. Human error is inherent because raw data for artificial intelligence learning must be processed and inspected manually to secure data quality for the advancement of AI learning. In this study, we examine the necessity of learning data management before machine learning by analyzing inaccurate cases of AI learning data and cyber security attack method through the approach from cyber security perspective. In order to verify the learning data integrity, this paper presents the direction of data-preserving artificial intelligence system, a blockchain-based learning data environment model. The proposed method is expected to prevent the threats such as cyber attack and data corruption in providing and using data in the open network for data processing and raw data collection.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.390-399
• /
• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.2
• /
• pp.97-102
• /
• 2023

Numerical simulations have been performed to investigate the hydrodynamic characteristics of the rudder since they play an important role in naval architecture fields. Although some values such as hydrodynamics forces can be measured easily in the towing tanks, it is difficult to obtain the detailed information of the flow fields such as pressure distribution, velocity distribution, vortex generation from experiments. In the present study, the effects of hydrodynamic coefficients and Reynolds number acting on the rudder were studied by using Computational Fluid Dynamics(CFD). Ansys fluent, one of commercial CFD solvers, solves the Navier-Stokes equations and the k-epsilon turbulence model is selected for the viscous model to solve RANS equations. At first, drag coefficients and lift coefficient for different angle of attack are obtained by using a CFD commercial code for KCS rudder. Secondly, the 2-D lift coefficients and drag coefficients are compared with 3-D coefficients at the same conditions. Thirdly, the effects of Reynolds number on the hydrodynamic forces are investigated.