• Journal of KIISE:Computing Practices and Letters
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• v.6 no.2
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• pp.120-134
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• 2000

In this paper we present a Distributed Object oriented Virtual computing Environment, called DOVE which consists of autonomous distributed objects interacting with one another via method invocations based on a distributed object model. DOVE appears to a user logically as a single virtual computer for a set of heterogeneous hosts connected by a network as if objects in remote site reside in one virtual computer. By supporting efficient parallelism, heterogeneity, group communication, single global name service and fault-tolerance, it provides a transparent and easy-to-use programming environment for parallel applications. Efficient parallelism is supported by diverse remote method invocation, multiple method invocation for object group, multi-threaded architecture and synchronization schemes. Heterogeneity is achieved by automatic data arshalling and unmarshalling, and an easy-to-use and transparent programming environment is provided by stub and skeleton objects generated by DOVE IDL compiler, object life control and naming service of object manager. Autonomy of distributed objects, multi-layered architecture and decentralized approaches in hierarchical naming service and object management make DOVE more extensible and scalable. Also,fault tolerance is provided by fault detection in object using a timeout mechanism, and fault notification using asynchronous exception handling methods

• Journal of the Korean Wood Science and Technology
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• v.38 no.6
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• pp.526-531
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• 2010

For efficient use of wood, it is important to control moisture of wood in processing wood. Near-infrared (NIR) spectroscopy can be used to estimate the physical and chemical properties of materials quickly and nondestructively. In this study, it was intended to measure the moisture contents on the surface of wood using NIR spectroscopy coupled with multivariate analytic statistical techniques. Because NIR spectroscopy is affected by the chemical components of the specimens and contains signal noise, a regression model for detecting moisture content of wood was established after carrying out several numerical pretreatments such as Smoothing, Derivative and Normalization in this study. It shows that the regression model using NIR absorbance in the range of 750~2,500 nm predicts the actual surface moisture content very well. Near-infrared spectroscopy technique developed in this study is expected to improve a technology to control moisture content of wood in using and drying process.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.73-82
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• 2011

The location-based service is emerging again to the public attention. The location recognition environment up-to-now has been studied with its focus only on a person, an object or a moving object. However, this study proposes a location recognition model that serves to recognize and track, in real time, multiple passengers in a moving vehicle. Identifying the locations of passengers can be classified into two classes: one is to use the high price terminal with GPS function, and the other is to use the economic price compact terminal without GPS function. Our model enables the simple compact terminal to provide effective location recognition under the on-boarding situation by transmitting messages through an interface device and sensor networks for a vehicle equipped with GPS. This technology reduces transmission traffic after detecting the condition of a vehicle (being parked or running), because it does not require transmission/receiving of information on the locations of passengers who are confined in a vehicle when the vehicle is running. Also it extends battery life by saving power consumption of the compact terminal. Hence, we carried out experiments to verify its serviceability by materializing the efficient tracking system for passengers with the detection algorithm of a stopping vehicle proposed in this study. Moreover, about 200 experiments using the system designed with this technology proved successful recognition on on-boarding and alighting of passengers with the maximum transmission distance of 12 km. In addition to this, the running recognition tests showed the test with the detection algorithm of a stopping vehicle has reduced transmission traffic by 41.6% compared to the algorithm without our model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.847-857
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• 2018

As the number of malicious code increases steeply, cyber attack victims targeting corporations, public institutions, financial institutions, hospitals are also increasing. Accordingly, academia and security industry are conducting various researches on malicious code detection. In recent years, there have been a lot of researches using machine learning techniques including deep learning. In the case of research using Convolutional Neural Network, ResNet, etc. for classification of malicious code, it can be confirmed that the performance improvement is higher than the existing classification method. However, one of the characteristics of the target attack is that it is custom malicious code that makes it operate only for a specific company, so it is not a form spreading widely to a large number of users. Since there are not many malicious codes of this kind, it is difficult to apply the previously studied machine learning or deep learning techniques. In this paper, we propose a method to classify malicious codes when the amount of samples is insufficient such as targeting type malicious code. As a result of the study, we confirmed that the accuracy of 97% can be achieved even with a small amount of data by applying the Memory Augmented Neural Networks model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.901-908
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• 2011

For the available grid environmental realization, the resource supply PC must have to provide an appropriate security function of their operation environments. SKY@HOME is a kind of the grid computing environments. If this has not supervised by administrator handling smoothly, it is inherently vulnerable state to the security level of the grid environments, because the resource supply PC is not update a security function without delay. It is also have the troublesome problems which have to install of an additional security program for support the appropriate security. This paper proposes an integration security model on the policy-based that provides an update each level according to the situation of the resource supply PC for improving its problems as a security aspect of the SKY@HOME. This model analyzes the security state of the resource supply PC respectively, and then the result is available to provide an appropriate security of the resource supply PC using an integration security model. The proposed model is not need additionally to buy and install the software, because it is provided the security management server oriented service. It is also able to set up the suit security function of a characteristic of the each resource supply PC. As a result, this paper clearly show the participation of resource supply PC improved about 20%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.463-466
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• 2010

In this paper, CCTV based fire surveillance system should aim to development. Advantages and Disadvantages analyzed of Existing sensor-based fire surveillance system and video-based fire surveillance system. To national support U-City, U-Home, U-Campus, etc, spread the ubiquitous environment appropriate to fire surveillance system model and a fire judgement technology. For this study, Microsoft LifeCam VX-1000 using through the capturing images and analyzed for apple and tomato, Finally we used H.264. The client uses the Linux OS with ARM9 S3C2440 board was manufactured, the client's role is passed to the server to processed capturing image. Client and the server is basically a 1:1 video communications. So to multiple receive to video multicast support will be a specification. Is fire surveillance system designed for multiple video communication. Video data from the RGB format to YUV format and transfer and fire detection for Y value. Y value is know movement data. The red color of the fire is determined to detect and calculate the value of Y at the fire continues to detect the movement of flame.

• Convergence Security Journal
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• v.21 no.2
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• pp.11-18
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• 2021

To protect tangible and intangible assets, most of the companies are conducting information protection monitoring by using various security equipment in the IT service network. As the security equipment that needs to be protected increases in the process of upgrading and expanding the service network, it is difficult to monitor the possible exposure to the attack for the entire service network. As a countermeasure to this, various studies have been conducted to detect external attacks and illegal communication of equipment, but studies on effective monitoring of the open service ports and construction of illegal communication monitoring system for large-scale service networks are insufficient. In this study, we propose a framework that can monitor information leakage and illegal communication attempts in a wide range of service networks without large-scale investment by analyzing 'Netflow statistical information' of backbone network equipment, which is the gateway to the entire data flow of the IT service network. By using machine learning algorithms to the Netfllow data, we could obtain the high classification accuracy of 94% in identifying whether the Telnet service port of operating equipment is open or not, and we could track the illegal communication of the damaged equipment by using the illegal communication history of the damaged equipment.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.41-49
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• 2021

All application programs, including malware, call the Application Programming Interface (API) upon execution. Recently, using those characteristics, attempts to detect and classify malware based on API Call information have been actively studied. However, datasets containing API Call information require a large amount of computational cost and processing time. In addition, information that does not significantly affect the classification of malware may affect the classification accuracy of the learning model. Therefore, in this paper, we propose a method of extracting a essential feature set after reducing the dimensionality of API Call information by applying various feature selection methods. We used CICAndMal2020, a recently announced Android malware dataset, for the experiment. After extracting the essential feature set through various feature selection methods, Android malware classification was conducted using CNN (Convolutional Neural Network) and the results were analyzed. The results showed that the selected feature set or weight priority varies according to the feature selection methods. And, in the case of binary classification, malware was classified with 97% accuracy even if the feature set was reduced to 15% of the total size. In the case of multiclass classification, an average accuracy of 83% was achieved while reducing the feature set to 8% of the total size.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.7
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• pp.918-925
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• 2017

Multiple reflections on exterior equipment with complex shape on naval ships cause unexpectedly high Radar Cross Section (RCS) distributions, and the directions of reradiated electromagnetic waves are hard to predict. Therefore, the optimum arrangement of exterior equipments should be considered according to the Radar Absorbing Structure (RAS) method. In this paper, the optimum arrangement for exterior equipments was determined to reduce multiple reflections and RCS even with complex shapes. The sequential descending arrangement method was used to establish an optimum arrangement algorithm. An LCS-2 type model was selected for optimum exterior equipment arrangements. In order to reduce computational cost, RCS distributions and multiple reflection path analysis of exterior equipments was carried out to select exterior equipments for optimum arrangement, and an optimum arrangement was determined to find positions with minimum RCS values. Also, the RCS reduction effect was analyzed using detectable radar range.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.5
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• pp.550-559
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• 2019

The sinking of the M/V SEWOL in April 2014 was not a mere marine accident, but a marine catastrophe. This grim case developed into a social tragedy that impinged the national sentiment and communal integrity. It is imperative that thorough provisions and measures be outlined at the national level with regard to massive marine accidents, oil pollution, and natural disasters that might critically affect government affairs. Pivoting on "The Black Swan Theory," a concept of improperly rationalizing a national crisis based on uncertainties, this research assesses a variety of response strategies that minimize the national economic and social damage caused by a large-scale marine disaster. Along with the effort of minimizing any potential defects in each protective barrier, the "Black Swan Detection System of the Marine Disaster" needs to be incorporated to prevent cases wherein such defects lead to an actual crisis. Maritime safety must be systematically unified under a supervisory organization, and a structure for maritime crisis on-scene command and cooperation must likewise be established in order that every force on the scene of a marine disaster may act effectively and consistently under the direction of an on-scene commander.