• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.449-456
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• 2018

Many organizations divide the network to manage the network in order to prevent the leakage of internal data between separate organizations / departments by sending and receiving unnecessary traffic. The most fundamental network separation method is based on physically separate equipment. However, there is a case where a network is divided and operated logically by utilizing a virtual LAN (VLAN) network access control function that can be constructed at a lower cost. In this study, we first examined the possibility of bypassing the logical network separation through VLAN ID scanning and double encapsulation VLAN hopping attack. Then, we showed and implemented a data leak scenario by utilizing the acquired VLAN ID. Furthermore, we proposed a simple and effective technique to detect and prevent the double encapsulation VLAN hopping attack, which is also implemented for validation. We hope that this study improves security of organizations that use the VLAN-based logical network separation by preventing internal data leakage or external cyber attack exploiting double encapsulation VLAN vulnerability.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.379-388
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• 2011

At present, GEOBIA (Geographic Object-based Image Analysis), heir of OBIA (Object-based Image Analysis), is regarded as an important methodology by object-oriented paradigm for remote sensing, dealing with geo-objects related to image segmentation and classification in the different view point of pixel-based processing. This also helps to directly link to GIS applications. Thus, GEOBIA software is on the booming. The main theme of this study is to look into the applicability of geo-spatial processing open source to GEOBIA. However, there is no few fully featured open source for GEOBIA which needs complicated schemes and algorithms, till It was carried out to implement a preliminary system for GEOBIA running an integrated and user-oriented environment. This work was performed by using various open sources such as OTB or PostgreSQL/PostGIS. Some points are different from the widely-used proprietary GEOBIA software. In this system, geo-objects are not file-based ones, but tightly linked with GIS layers in spatial database management system. The mean shift algorithm with parameters associated with spatial similarities or homogeneities is used for image segmentation. For classification process in this work, tree-based model of hierarchical network composing parent and child nodes is implemented by attribute join in the semi-automatic mode, unlike traditional image-based classification. Of course, this integrated GEOBIA system is on the progressing stage, and further works are necessary. It is expected that this approach helps to develop and to extend new applications such as urban mapping or change detection linked to GIS data sets using GEOBIA.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.55-64
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• 2018

In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.819-834
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• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2665-2691
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• 2024

The philosophy of Zero Trust in cybersecurity lies in the notion that nothing assumes to be trustworthy by default. This drives defense organizations to modernize their cybersecurity architecture through integrating with the zero-trust principles. The enhanced architecture is expected to shift protection strategy from static and perimeter-centric protection to dynamic and proactive measures depending on the logical contexts of users, assets, and infrastructure. Given the domain context of defense environment, we aim three challenge problems to tackle and identify four technical approaches by the security capabilities defined in the Zero Trust Architecture. First approach, dynamic access control manages visibility and accessibility to resources or services with Multi Factor Authentication and Software Defined Perimeter. Logical network separation approach divides networks on a functional basis by using Software Defined Network and Micro-segmentation. Data-driven analysis approach enables machine-aided judgement by utilizing Artificial Intelligence, User and Entity Behavior Analytics. Lastly, Security Awareness approach observes fluid security context of all resources through Continuous Monitoring and Visualization. Based on these approaches, a comprehensive study of modern technologies is presented to materialize the concept that each approach intends to achieve. We expect this study to provide a guidance for defense organizations to take a step on the implementation of their own zero-trust architecture.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.71-77
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• 2014

In this paper, we propose an improved numeric character recognition method which can recognize numeric characters well under low-illuminated and shade-illuminated environment. The LN(Local Normalization) preprocessing method is used in order to enhance low-illuminated and shade-illuminated image quality. The reading area is detected using line segment information extracted from the illumination-normalized meter images, and then the three-phase procedures are performed for segmentation of numeric characters in the reading area. Finally, an efficient hybrid classifier is used to classify the segmented numeric characters. The proposed numeric character classifier is a combination of multi-layered feedforward neural network and template matching module. Robust heuristic rules are applied to classify the numeric characters. Experiments using meter image database were conducted. Meter image database was made using various kinds of meters under low-illuminated and shade-illuminated environment. The experimental results indicates the superiority of the proposed numeric character recognition method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.457-469
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• 2018

This study aimed to investigate the fundamental reasons for the presence of multi-homed host and the risks associated with such risky system. Furthermore, multi-homed host detection methods that have been researched and developed so far were compared and analyzed to determine areas for improvement. Based on the results, we propose the model of an improved automatic detection system and we implemented it. The experimental environment was configured to simulate the actual network configuration and endpoints of an organization employing network segmentation. And the functionality and performance of the detection system were finally measured while generating multi-homed hosts by category, after the developed detection system had been installed in the experiment environment. We confirmed that the system work correctly without false-positive, false-negative in the scope of this study. To the best of our knowledge, the presented detection system is the first academic work targeting multi-homed host under agent-based.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.341-343
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• 2012

This study introduces a method for computation of process and operation gap in the specific construction operation(i.e., RC frame construction applying a block-grouping scheme) using CYCLONE-based simulation modeling and analysis technique. Since uncertainty of construction environment exists, a thoughtful production planning is required to effectively deal with a risk resulting in schedule delay in advance. This study presents the concepts of a time delay occurred in a process level and operation level in a operation model, and a method of measuring gap-times in each level while the simulation progresses. It helps a site manager to decide how many segmentation in a construction block is suitable for eliminating unproductive time-delays under the constrained resources (e.g., laborer, equipment). A case study presents a network model representing a three segmented RC frame work, and result obtained from the simulation experiment.

• ETRI Journal
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• v.37 no.5
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• pp.1044-1054
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• 2015

The smart media (SM) industry has demonstrated that it has the characteristics to increase user innovative activities, enhance open innovativeness, and increase the segmentation of innovation value. This study introduces and evaluates an innovation system that reflects the characteristics of the SM industry. We categorize the SM industry into hardware, network, platform, and content industries and perform an AHP analysis (based on a survey of 96 experts) to evaluate the relative importance of the factors/factor groups affecting the creation of innovation. The results show that 'collaboration activity" is a more important factor than other innovation factor groups (financial support, R&D, policy environment, human resources) in the SM industry. The results also show that the important factors/factor groups differ by industry.