• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.4
• /
• pp.785-796
• /
• 2012

In this paper, we propose an effective Behavior-based detection technique using the frequency of system calls to detect malicious code, when the number of training data is fewer than the number of properties on system calls. In this study, we collect the Native APIs which are Windows kernel data generated by running program code. Then we adopt the normalized freqeuncy of Native APIs as the basic properties. In addition, the basic properties are transformed to new properties by GLDA(Generalized Linear Discriminant Analysis) that is an effective method to discriminate between malicious code and normal code, although the number of training data is fewer than the number of properties. To detect the malicious code, kNN(k-Nearest Neighbor) classification, one of the bayesian classification technique, was used in this paper. We compared the proposed detection method with the other methods on collected Native APIs to verify efficiency of proposed method. It is presented that proposed detection method has a lower false positive rate than other methods on the threshold value when detection rate is 100%.

• Journal of Korea Water Resources Association
• /
• v.53 no.10
• /
• pp.889-902
• /
• 2020

This study was performed in Seolmacheon basin to evaluate the adequacy of GLDAS (Global Land Data Assimilation System) and GLEAM (Global Land Evaporation Amsterdam Model) evapotranspiration data. The verification data necessary for the evaluation of adequacy were calculated after processing the latent heat flux data produced in the Seolmacheon basin with the Koflux program. In order to gap-fill the empty period, alternative evapotranspiration was calculated in three ways: FAO-PM (Food and Agriculture Organization-Penman Monteith), MDV (Mean Diurnal Variation) and Kalman Filter. This study selected Kalman Filter method as the data gap-filling method because it showed the best Bias and RMSE among the three methods. The amount of GLDAS spatial evapotranspiration was calculated as Noah (version 2.1) with a time interval of 3 hours and a spatial resolution of 0.25°. The amount of GLEAM spatial evapotranspiration was calculated using GLEAM (version 3.1a). This study evaluated the spatial evapotranspiration of GLDAS and GLEAM as the evapotranspiration based on eddy covariance. As a result of evaluation, GLDAS spatial evapotranspiration showed better results than GLEAM. Accordingly, in this study, the GLDAS method was proposed as a method for calculating the amount of spatial evapotranspiration in the Seolmacheon basin.

• Journal of Korea Water Resources Association
• /
• v.57 no.5
• /
• pp.333-346
• /
• 2024

High-resolution medium-range streamflow prediction is crucial for sustainable water quality and aquatic ecosystem management. For reliable medium-range streamflow predictions, it is necessary to understand the characteristics of forcings and to effectively utilize weather forecast data with low spatio-temporal resolutions. In this study, we presented a comparative analysis of medium-range streamflow predictions using the distributed hydrological model, WRF-Hydro, and the numerical weather forecast Global Data Assimilation and Prediction System (GDAPS) in the Geumho River basin, Korea. Multiple forcings, ground observations (AWS&ASOS), numerical weather forecast (GDAPS), and Global Land Data Assimilation System (GLDAS), were ingested to investigate the performance of streamflow predictions with highresolution WRF-Hydro configuration. In terms of the mean areal accumulated rainfall, GDAPS was overestimated by 36% to 234%, and GLDAS reanalysis data were overestimated by 80% to 153% compared to AWS&ASOS. The performance of streamflow predictions using AWS&ASOS resulted in KGE and NSE values of 0.6 or higher at the Kangchang station. Meanwhile, GDAPS-based streamflow predictions showed high variability, with KGE values ranging from 0.871 to -0.131 depending on the rainfall events. Although the peak flow error of GDAPS was larger or similar to that of GLDAS, the peak flow timing error of GDAPS was smaller than that of GLDAS. The average timing errors of AWS&ASOS, GDAPS, and GLDAS were 3.7 hours, 8.4 hours, and 70.1 hours, respectively. Medium-range streamflow predictions using GDAPS and high-resolution WRF-Hydro may provide useful information for water resources management especially in terms of occurrence and timing of peak flow albeit high uncertainty in flood magnitude.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.227-227
• /
• 2016

일반적으로 가뭄은 신뢰성 높고 활용이 쉬운 강수량 자료를 활용하여 판단되고 있으나, 복합적인 대응을 하기 위해서는 증발산량, 토양수분 등 다양한 변수를 고려해야 한다. 이러한 수문기상정보들은 관측자료의 자료 확보기간이 통계 분석을 하기에 짧거나, 시공간적 대표성 부족 등의 단점이 있다. 이러한 문제점을 극복하기 위해 지면모델이 대안으로 널리 활용중이나, 이를 실제로 가뭄에 활용한 응용연구는 상대적으로 부족한 실정이다. 본 연구에서는 미국 NASA의 전지구지표자료동화체계 GLDAS (Global Land Data Assimilation System) 산출물을 활용하여 지면모델 기반의 수문기상정보를 국내 가뭄감시 연구에 적용하고자 하였다. 이를 위해, GLDAS 프로젝트를 통해 제공되는 다중모델 기반의 증발산량, 토양수분 결과를 비교 분석하고 이를 직접 활용할 수 있는 가뭄판단 지수에 적용하여 성능을 검토하였다. 이를 통해 GLDAS 산출 정보가 가뭄판단에 있어 발휘하는 성능을 평가함으로써, 향후 본원에서 구축할 지면 모델 앙상블 시스템의 가뭄감시정보 산출의 효과를 간접적으로 검토하고자 한다.

• Journal of Korea Water Resources Association
• /
• v.45 no.8
• /
• pp.805-814
• /
• 2012

Most hydrologic data are obtained by ground observations. New observation methods are needed for some regions to overcome difficulties in accessibility and durability of long-term observation. In 2002, NASA launched twin satellites named GRACE which were designed to measure the gravitational field of the earth. Using the GRACE monthly gravity level-2 data, we calculated terrestrial water storage change (TWSC) of the Korean peninsula in various spatial smoothing radii (0 km, 300 km, 500 km). For the validation of GRACE-based TWSC, we compared it with land-based TWSC which was obtained using the ground observation data: precipitation and evaporation from WAMIS, and runoff from GLDAS. According to the mean square-error test, GRACE-based TWSC best fits the land-based one at 500 km smoothing radius. The variation of the terrestrial water storage in the Korean peninsula turned out to be 0.986 cm/month, which means that appropriate measures should be prepared for sustainable water resources management.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.467-467
• /
• 2015

Sustainable water resource management requires the assessment of hydrological variability in response to climate fluctuations and anthropogenic activities. Determining quantitative estimates of water balance and total basin discharge are of utmost importance to understand the variations within a basin. Hard-to-reach areas with few infrastructures, coupled with lengthy administrative procedures makes in-situ data collection and water management processes very difficult and unreliable. In this study, the hydrological behavior of Lake Chad whose extent, extreme climatic and environmental conditions make it difficult to collect field observations was examined. During a 10 year period [January 2003 to December 2013], dataset from space-borne and global hydrological models observations were analyzed. Terrestial water storage (TWS) data retrieved from Gravity Recovery and Climate Experiment (GRACE), lake level variations from Satellite altimetry, water fluxes and soil moisture from Global Land Data Assimilation System (GLDAS) were used for this study. Furthermore, we combined altimetry lake volume with TWS over the lake drainage basin to estimate groundwater and soil moisture variations. This will be validated with groundwater estimates from WaterGAP Global Hydrology Model (WGHM) outputs. TWS showed similar variation patterns Lake water level as expected. The TWS in the basin area is governed by the lake's surface water. As expected, rainfall from GLDAS precedes GRACE TWS with a phase lag of about 1 month. Estimates of groundwater and soil moisture content volume changes derived by combining altimetric Lake Volume with TWS over the drainage basin are ongoing. Results obtained shall be compared with WaterGap Hydrology Model (WGHM) groundwater estimate outputs.

• Journal of Korea Water Resources Association
• /
• v.52 no.1
• /
• pp.11-19
• /
• 2019

In this study, the actual evapotranspiration products of Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM) and MOD16, which are satellite- and reanalysis-based dataset, were validated at the flux tower sites (i.e., CFK and SMK) managed by Korea Institute of Hydrological Survey, and the uncertainty and correlation analysis were conducted using Triple Collocation (TC) method. The result of validation with the flux tower showed better agreement in the order of GLEAM> GLDAS>MOD16. At the result of three combinations (S1: flux tower vs. GLDAS vs. MOD16, S2: flux tower vs. GLDAS vs. GLEAM, S3: flux tower vs. GLEAM vs. MOD16), the order of best to worst is GLEAM, GLDAS, MOD16, and flux tower for CFK (GLDAS> GLEAM>MOD16>flux tower for SMK). Since the error variance and correlation coefficients of the flux tower show relatively worse performance in TC analysis than the other products, By applying TC method to three products (GLDAS vs. GLEAM vs. MOD16), the uncertainty of each dataset were evaluated at the Korean Peninsula, As a results, the GLDAS and GLEAM performed reasonable performance (low error variance and high correlation coefficient), whereas results of MOD16 showed high error variance and low correlation coefficient at the cropland.