• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Korean Journal of Mathematics
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• v.24 no.3
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• pp.409-445
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• 2016

The class $\mathcal{W}^{\delta}_{\beta}({\alpha},{\gamma})$ defined in the domain ${\mid}z{\mid}$ < 1 satisfying $Re\;e^{i{\phi}}\((1-{\alpha}+2{\gamma})(f/z)^{\delta}+\({\alpha}-3{\gamma}+{\gamma}\[1-1/{\delta})(zf^{\prime}/f)+1/{\delta}\(1+zf^{\prime\prime}/f^{\prime}\)\]\)(f/z)^{\delta}(zf^{\prime}/f)-{\beta}\)$ > 0, with the conditions ${\alpha}{\geq}0$, ${\beta}$ < 1, ${\gamma}{\geq}0$, ${\delta}$ > 0 and ${\phi}{\in}{\mathbb{R}}$ generalizes a particular case of the largest subclass of univalent functions, namely the class of $Bazilevi{\check{c}}$ functions. Moreover, for 0 < ${\delta}{\leq}{\frac{1}{(1-{\zeta})}}$, $0{\leq}{\zeta}$ < 1, the class $C_{\delta}({\zeta})$ be the subclass of normalized analytic functions such that $Re(1/{\delta}(1+zf^{\prime\prime}/f^{\prime})+1-1/{\delta})(zf^{\prime}/f))$ > ${\zeta}$, ${\mid}z{\mid}$<1. In the present work, the sucient conditions on ${\lambda}(t)$ are investigated, so that the non-linear integral transform $V^{\delta}_{\lambda}(f)(z)=\({\large{\int}_{0}^{1}}{\lambda}(t)(f(tz)/t)^{\delta}dt\)^{1/{\delta}}$, ${\mid}z{\mid}$ < 1, carries the fuctions from $\mathcal{W}^{\delta}_{\beta}({\alpha},{\gamma})$ into $C_{\delta}({\zeta})$. Several interesting applications are provided for special choices of ${\lambda}(t)$. These results are useful in the attempt to generalize the two most important extremal problems in this direction using duality techniques and provide scope for further research.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1203-1208
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• 2007

In the past decades, complain about ground vibration and noise induced by pile driving has been quickly increased. Because of that, auger drilled piling methods have frequently used specially in urban area. However, the present auger drilled piling methods induce inevitable ground disturbance as well as a certain degree of vibration and noise due to the final hammering. For these reasons, a new auger drilled piling method is required to be developed. This paper introduces PDT(Pulse Discharge Technology) piling method and presents the characteristics of bearing capacity. The PDT piling method is to install in-situ piles using electric power so called Pulse. The pile installed by PDT appears to be able to develop shaft and end bearing capacity efficiently. This paper introduces PDT(Pulse Discharge Technology) piling method, which is the 512nd new construction technology. The PDT piling method is to install in-situ piles using electrical power so called Pulse power. The pulse power is physical value that indicates the energy change per unit time(dE/dt). Since the pulse power is to push ground, using the pulse power is enable a hole to be expanded as well as the ground to be improved by compaction. Therefore, The pile installed by PDT appears to be able to develop shaft and end bearing capacity efficiently. In this study, couples of pile loading tests were carried out to figure out whether or not the PDT piling method is applicable to constructions like rail road facility. As a result, it was concluded that the PDT piling technique meet the requirements for such a rail road related construction.

• YAKHAK HOEJI
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• v.40 no.1
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• pp.95-101
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• 1996

The in vitro antibacterial activities of LB10522, a new catechol-substituted cephalosporin, were compared with those of cefpirome, ceftazidime, ceftriaxone, and cefoperaz one against clinical isolates and laboratory standard anaerobes. LB10522 had broad spectrum antibacterial activities against both gram-positive and gram-negative microorganisms. It was most active against gram-positve bacteria among the reference cephalosporins tested. Against gram-negative strains such as the family Enterobacteriaceae, LB10522 showed an activity comparable to that of cefpirome. But LB10522 was more potent than ceftazidime, ceftriaxone and cefoperazone. In particular, Pseudomonas aeruginosa was highly susceptible to LB10522, which was 32-fold and 64-fold more active than ceftazidime and cefpirome, respectively. Against anaerobic strains, the activity of LB10522 was similar to those of reference compounds. LB10522 exhibited potent therapeutic activities against experimental local infections in mice. The therapeutic effect of LB10522 against urinary tract infection (UTI) caused by P. aeruginosa 1912E in mice was superior to that of cefpirome. Against experimental respiratory tract infection (RTI) caused by K. pneumoniae DT-S in mice, LB10522 was as effective as cefpirome. The in vivo efficacy of LB10522 was correlated well with its in vitro activity.

• Applied Biological Chemistry
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• v.26 no.1
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• pp.1-7
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• 1983

The hydration of two japonica(Akibare and Milyang 15) and four indica(Milyang 30, Suweon 287, Suweon 294 and Iri 342) rice varieties was investigated in terms of mathematical rate equation. The hydration rate at temperatures of $4{\sim}32^{\circ}C$ was examined by a weighing method. The absorption of water was directly proportiponal to the square root of the hydration time(t) and was described by the diffusion equation: $1-\bar{M}=(2/\sqrt{\pi})(S/V)\;\sqrt{Dt},\;where\;\bar{M}$ is dimensionless moisture ratio, S/V is the surface-to-volume ratio and D is diffusion coefficient. The average D value was given by the Arrhenius relation: $D=D_0\;\exp(-E_a/TR)$. The activation energy was $4{\sim}5kcal/mole$. The rice samples could be classified into three groups based on hydration kinetics: Milyang 30-Suweon 287; Akibare-Milyang 15; and Suweon 294-Iri 342.

• Korean Journal of Optics and Photonics
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• v.12 no.6
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• pp.452-459
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• 2001

ICC profiles of output devices are necessary to solve the problem of color inconsistency between output devices. Therefore, output device manufacturers are offering ICC profiles that encode each model\`s color characteristics according to the specification decided in ICC (International Color Consortium). In this study, a program that can decode data of ICC profile was composed and evaluated the present condition of ICC profile files of domestic CRT monitor. As a result of comparing the ICC profiles of various model\ulcorner selling since 1999 from LG and Samsung companies, it was found that ICC profiles that are made at a similar time are the same regardless of model\`s specification and in some profiles, extraordinary data are saved. Accordingly, it can be said that current ICC profiles are made independently of the real monitor\`s color characteristics. This paper shows how color characteristics of a real monitor are affected by the control of brightness and color temperature, and proposes that the ICC profile has to be made from the data. measured in the optimum brightness state at each color temperature setting.

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.3
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• pp.454-464
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• 1987

The objective of this research is to analyze and evaluate the dynamic flow curve of metals under impact loading at both high strain rate (.epsilon.=1/h dh/dt > 10$\^$3/m/s/m) and large strain (.epsilon.=In h/h$\_$0/ > 1.0). A test method for dynamic compression of metal disc is described. The velocity of the striker face and the force on the anvil are measured during the impact period. From these primitive data the axial stress, strain, and strain rate of the disc are obtained. The Strain rate is determined by the striker velocity divided by the specimen height. This gives a slightly increasing strain rate over most of the deformation period. Strain rates of 100 to 10,000 per second are achieved. Attainable final strains are 150%. A discussion of several problem areas is presented. The friction on the specimen surfaces, the determination of the frictional coefficient, the influence of the specimen geometry (h$\_$0//d$\_$0/ ratio) on the friction effect, the lock-up condition for a given configuration, the friction correction factor, and the evaluation of several lubricants are given. The flow function(stress verus strain) is dependent on the material condition(e.g., prior cold work), specimen geometry, strain rate, and temperature.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.33-41
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• 2022

Infrastructure procurement has been a major engagement route between China and Africa. This contributes immensely to the gradual infrastructure development seen on the continent. However, maturing discourse purports that these infrastructure collaborations lack intentionality in the continuous development of strategic guidelines and policies for effective implementation despite their uniqueness and criticality. This study proposes that an efficient approach to policy recommendations is through the political and economic analysis (PEA) of these partnerships using public-private partnership (PPP) optics. Unquestionably, these partnerships are representative of the concept of diplomatic transnational public-private partnership (DT-PPP) where infrastructure is procured through the collaboration of public (African governments) and private sector (Chinese state-owned corporations) who provide the managerial, financial, and technical resources for the project implementation. Given the quest for sustainable win-win, this study identifies strategies towards the realization of win-win in the implementation (i.e enablers of win-win) such that fairness and co-benefit, as well as interests, will be achieved. Thus, based on the PEA framework, case scenarios from Ghana and Nigeria using expert interviews identify the criticalities and best practices for the realization of these enablers at the development phase. Findings indicate more effort is required of the public sector (African host countries) in terms of people, structure/institutions, and the implementation processes. Recommendations include improvement of environmental management structures, contract administration procedures, external stakeholders/local community engagement mechanisms, knowledge and technology transfer procedures, and sector-based project operation and maintenance culture and systems. Additionally, actors must have emotional intelligence, good problem-solving abilities, and overall ensure cordial relationships for continued bilateral cooperation.

• Journal of Korean Association for Spatial Structures
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• v.24 no.3
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• pp.43-51
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• 2024

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson's ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.