• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.116-118
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• 2012

안드로이드 앱 시장이 활성화되면서, 안드로이드 앱의 불법복제나 역공학 공격으로 인한 피해가 증가하고 있다. 앱 불법복제는 앱 판매 수익의 저하뿐만 아니라 개발자의 의지를 뺏고 개발 노력에 대해 상대적 박탈감을 주게 된다. 자바 프로그램의 경우 역공학으로 인해 바이트 코드에 존재하는 핵심 알고리즘이 쉽게 노출되어 지적재산권이 유출될 수 있다는 점에서 개발자나 개발사에게 심각한 위협이 되고 있다. 본 논문에서는 안드로이드 환경에서 앱에 대한 역공학 공격의 위협을 보이고, 역공학 방지 기법인 RACC를 제안한다. RACC는 보호할 핵심 클래스를 앱으로 부터 추출하여 바이트 코드 형태로 안전한 원격 서버에 관리하여 수행하며, 스마트폰(클라이언트)에는 저장하지 않는다. 스마트폰 앱이 해당 핵심 클래스를 호출하면, 그 호출이 원격 서버로 전송되어 수행된 후 결과가 스마트폰에 반환된다. 이처럼 핵심 클래스 코드가 클라이언트에 직접 노출 없이 원격지에서 관리되고 수행됨으로써 역공학 공격을 원천적으로 방지한다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.1-7
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• 1992

The conformance test is a methodology which verifies whether the Implementation Under Test (IUT) conforms to its standard specification or not. In this paper, first, the Conformance Test System(CTS) is implemented in prototype including verification test type, black-box test strategy, conformance test class, remote test structure, and IUT which is described by the ISO Estelle among Formal Description Techniques. Second, through this prototype the proposed system is experimented. The prototype is composed of two modules. The first module is the Estelle-to-Pascal preprocessor which converts the Estelle program to the corresponding Pascal program because the experimental IUT was described using the unexecutable code form of Estelle before being tested by the TD(Test Driver). The TD, the second module, is based on the master-slave concept and plays important role is CTS. The TD acts as the master with the IUTd being the slave. The prototype system is implemented using Turbo-C, Turbo-Pascal and Turbo-Assembly.

• Journal of Environmental Science International
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• v.29 no.1
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• pp.25-32
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• 2020

The determination of soil parameters is important in predicting the simulated surface runoff using either a distributed or a lumped rainfall-runoff model. Soil characteristics can be collected using remote sensing techniques and represented as a digital map. There is no universal agreement with respect to the determination of a representative parameter from a gridded digital map. Two representative methods, i.e., arithmetic and predominant, are introduced and applied to both FLO-2D and HEC-HMS to improve the model's accuracy. Both methods are implemented in the Yongdam catchment, and the results show that the former seems to be more accurate than the latter in the test site. This is attributed to the high conductivity of the dominant soil class, which is A type.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.696-701
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• 2002

This paper describes some results of change detection in Tokyo metropolitan area, Japan , using the Landsat TM data, and methods to quantify the ground cover classes. The changes are analyzed using the measures of not only conventional spectral classes but also a set of landscape indices to describe spatial properties of ground cove types using fractal dimension of objects, entropy in the specific windows defining the neighbors of focusing locations. In order eliminate the seasonal radiometric effects on TM data, an automated class labeling method is also attempted. Urban areas are also delineated automatically by defining the boundaries of the urban area. These procedures for urban change detection were implemented by the unified image computing methods proposed by the author, they can be automated in coherent and systematic ways, and it is anticipated to automate the whole procedures. The results of this analysis suggest that Tokyo metropolitan area was extended to the suburban areas along the new transportation networks and the high density area of Tokyo were also very much extended during the period between 1985 and 1995.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.48-53
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• 1999

The paper presents an automated method for generating fuzzy rules and fuzzy membership functions for pattern classification from training sets of examples and an application to the land-cover classification. Initially, fuzzy subspaces are created from the partitions formed by the minimum and maximum of individual feature values of each class. The initial membership functions are determined according to the generated fuzzy partitions. The fuzzy subspaces are further iteratively partitioned if the user-specified classification performance has not been archived on the training set. Our classifier was trained and tested on patterns consisting of the DN of each band, (XS1, XS2, XS3), extracted from KITSAT-3 multispectral scene. The result represents that our classification method has higher generalization power.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.301-304
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• 2005

For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques or frequent episode mining in order to analyze an audit data. But, since they mainly analyze the average behaviour of user's activities, some anomalies can be detected inaccurately. Therefore, we propose an anomaly detection method that utilizes an associative classification for modelling intrusion detection. Finally, we proof that a prediction model built from associative classification method yields better accuracy than a prediction model built from a traditional methods by experimental results.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.713-716
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• 2005

In this paper, in order to identify and recognize attack patterns, we propose a Bayesian classification using frequent patterns. In theory, Bayesian classifiers guarantee the minimum error rate compared to all other classifiers. However, in practice this is not always the case owing to inaccuracies in the unrealistic assumption{ class conditional independence) made for its use. Our method addresses the problem of attribute dependence by discovering frequent patterns. It generates frequent patterns using an efficient FP-growth approach. Since the volume of patterns produced can be large, we propose a pruning technique for selection only interesting patterns. Also, this method estimates the probability of a new case using different product approximations, where each product approximation assumes different independence of the attributes. Our experiments show that the proposed classifier achieves higher accuracy and is more efficient than other classifiers.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.123-125
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• 2005

Many researchers conducted the effort for improving the classification accuracy of satellite image. Most of the study has used optical spectrum information of each pixel for image classification. By applying this method for high resolution satellite image, number of class becomes increase. This situation is remarkable for house, because the roof of house has variety of many colors. Even if the classification is carried out for many classes, roof color information of each house is not necessary. Most of the case, we need the information that object is house or not. In this study, we propose the method for detecting the object by using Genetic Algorithms (GA). Aircraft was selected as object. It is easy for this object to detect in the airport. An aircraft was taken as a template. Object image was taken from QuickBird. Target image includes an aircraft and Haneda Airport. Chromosome has four or five parameters which are composed of number of template, position (x,y), rotation angle, rate of enlarge. Good results were obtained in the experiment.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.321-339
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• 2017

In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MO-TRIBES/OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.

• Proceedings of the KIEE Conference
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• summer
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• pp.683-685
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• 2004

This paper introduces a basic design for the short-term load forecasting system using a commercial data base. The proposed system uses a hybrid load forecasting method using fuzzy linear regression for forecasting of weekends and Monday and general exponential smoothing for forecasting of weekdays. The temperature sensitive is used to improve the accuracy of the load forecasting during the summer season. MS-SQL Sever has been used a commercial data base for the proposed system and the database is operated by ADO(ActiveX Data Objects) and RDO(Remote Data Object). Database has been constructed by altering the historical load data for the past 38 years. The weather iDormation is included in the database. The developed short-term load forecasting system is developed as a user friendly system based on GUI(Graphical User interface) using MFC(Microsoft Foundation Class). Test results show that the developed system efficiently performs short-term load forecasting.