• Journal of the Korea Society of Computer and Information
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• v.21 no.7
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• pp.1-8
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• 2016

Nowadays, distributed denial of service (DDoS) attacks on web sites reward attackers financially or politically because our daily lifes tightly depends on web services such as on-line banking, e-mail, and e-commerce. One of DDoS attacks to web servers is called HTTP-GET flood attack which is becoming more serious. Most existing techniques are running on the application layer because these attack packets use legitimate network protocols and HTTP payloads; that is, network-level intrusion detection systems cannot distinguish legitimate HTTP-GET requests and malicious requests. In this paper, we propose a practical detection technique against HTTP-GET flood attacks, based on the access behavior of inline objects in a webpage using NetFlow data. In particular, our proposed scheme is working on the network layer without any application-specific deep packet inspections. We implement the proposed detection technique and evaluate the ability of attack detection on a simple test environment using NetBot attacker. Moreover, we also show that our approach must be applicable to real field by showing the test profile captured on a well-known e-commerce site. The results show that our technique can detect the HTTP-GET flood attack effectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.2
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• pp.38-44
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• 2013

In this paper, we designed and implemented a mobile video transmission device with 1080i resolution and 30fps frame rate using wireless multi-access technologies. The video acquisition delay time of 16us and jitter of 21ms for this equipment were obtained, respectively. The device is designed to access various communication networks. In order to evaluate the performance of its video transmission capability, a server and monitoring viewer are implemented. In the performance test using LTE network, 211ms delay time was obtained at 3Mbps transmission. Rendering of 1fps frame rate at the 265kbps transmission was confirmed for the transmission test using Inmarsat network.

• Journal of Korea Multimedia Society
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• v.21 no.7
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• pp.779-786
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• 2018

In this paper, we are applied a nerve network to allow for the reflection of data learning methods in their overall forms by using cluster data rather than data learning by the stages and then selected a nerve network model and analyzed its variables through learning by the cluster. The CkLR algorithm was proposed to analyze the reaction variables of clustering outcomes through an approach to the initialization of K-means clustering and build a model to assess the prediction rate of clustering and the accuracy rate of prediction in case of new data inputs. The performance evaluation results show that the accuracy rate of test data by the class was over 92%, which was the mean accuracy rate of the entire test data, thus confirming the advantages of a specialized structure found in the proposed learning nerve network by the class.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1989-2009
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• 2013

How to discover router vulnerabilities effectively and automatically is a critical problem to ensure network and information security. Previous research on router security is mostly about the technology of exploiting known flaws of routers. Fuzzing is a famous automated vulnerability finding technology; however, traditional Fuzzing tools are designed for testing network applications or other software. These tools are not or partly not suitable for testing routers. This paper designs a framework of discovering router protocol vulnerabilities, and proposes a mathematical model Two-stage Fuzzing Test Cases Generator(TFTCG) that improves previous methods to generate test cases. We have developed a tool called RPFuzzer based on TFTCG. RPFuzzer monitors routers by sending normal packets, keeping watch on CPU utilization and checking system logs, which can detect DoS, router reboot and so on. RPFuzzer' debugger based on modified Dynamips, which can record register values when an exception occurs. Finally, we experiment on the SNMP protocol, find 8 vulnerabilities, of which there are five unreleased vulnerabilities. The experiment has proved the effectiveness of RPFuzzer.

• Journal of the Korean Society for Library and Information Science
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• v.33 no.3
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• pp.145-167
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• 1999

This study aims to identify factors to be considered in designing and operating CD-ROM network systems efficiently and provide designers and operators with basic guidelines. In this study, the current status and problems of CD-ROM network systems at 72 domestic university libraries are examined, and user behavior and satisfaction of 64 actual users in 3 universities are analyzed. In addition, to examine the influenced correlation among the factors such as user satisfaction, system operating policy, system resources and system performance, $\chi^2(Chi-Square)$ test, F test and regression analysis are carried out and the factors influencing user satisfaction are examined.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2678-2685
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• 2020

The pipe bends and elbows in nuclear power plants (NPPs) are vulnerable to degradation mechanisms and can cause wall-thinning defects. As it is difficult to detect both the defects generated inside the wall-thinned pipes and the preliminary signs, the wall-thinning defects should be accurately estimated to maintain the integrity of NPPs. This paper proposes a deep fuzzy neural network (DFNN) method and estimates the collapse moment of wall-thinned pipe bends and elbows. The proposed model has a simplified structure in which the fuzzy neural network module is repeatedly connected, and it is optimized using the least squares method and genetic algorithm. Numerical data obtained through simulations on the pipe bends and elbows with extrados, intrados, and crown defects were applied to the DFNN model to estimate the collapse moment. The acquired databases were divided into training, optimization, and test datasets and used to train and verify the estimation model. Consequently, the relative root mean square (RMS) errors of the estimated collapse moment at all the defect locations were within 0.25% for the test data. Such a low RMS error indicates that the DFNN model is accurate in estimating the collapse moment for wall-thinned pipe bends and elbows.

• Animal cells and systems
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• v.16 no.2
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• pp.121-126
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• 2012

In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. Experimental data deduced by Fl$\ddot{u}$ckiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.17-21
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• 2002

There are many problems of interoperability between ITS system equipments since the protocol standard for a network center and a node has not defined. In this paper, we consider the use of the SNMP to solve the interoperability problem, and apply the SNMP in the case of a CCTV as an example. Also, we defined its corresponding MIBs and accomplished the interoperability test, where the method of controlling the CCTV is explained and the data transmitted on the network are shown in a hexadecimal format. Our results are expected to be helpful for a guideline or feasibility test as nationwide data to support the interoperability of ITS systems between a network center and nodes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.232-241
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• 2010

This paper describes the development of robust contingency analysis program for Korean Energy Management System. The important function of contingency analysis is to determine the bus/branch model for contingency, and to calculate the state of the power network based on the network model and topology output. In the proposed method, the bus/branch models for contingencies are determined exactly using a fast linked-list method based on the application common model database. To calculate the state of the power system included contingency, the full-decoupled powerflow approach, the partial powerflow method for contingencies and the proposed contingency screening algorithm are also used to contingency analysis. To verify the performance of the developed processor, we performed a file-based test using several structured input data and online test using the database which resides on memory. The results of these comprehensive tests showed that the developed processors can accurately calculate the power system contingency state from online data and can be applied to Korea Power Exchange system.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.223-235
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• 2008

This paper focuses on the application of artificial neural networks (ANN) for optimal design of tensegrity grid as light-weight roof structures. A tensegrity grid, 2 m ${\times}$ 2 m in size, is fabricated by integrating four single tensegrity modules based on half-cuboctahedron configuration, using galvanised iron (GI) pipes as struts and high tensile stranded cables as tensile elements. The structure is subjected to destructive load test during which continuous monitoring of the prestress levels, key deflections and strains in the struts and the cables is carried out. The monitored structure is analyzed using finite element method (FEM) and the numerical model verified and updated with the experimental observations. The paper then explores the possibility of applying ANN based on multilayered feed forward back propagation algorithm for designing the tensegrity grid structure. The network is trained using the data generated from a finite element model of the structure validated through the physical test. After training, the network output is compared with the target and reasonable agreement is found between the two. The results demonstrate the feasibility of applying the ANNs for design of the tensegrity structures.