• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.209-215
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• 2018

In this paper, we propose a method to detect and block Meltdown malicious code which is increasing rapidly using dynamic sandbox tool. Although some patches are available for the vulnerability of Meltdown attack, patches are not applied intentionally due to the performance degradation of the system. Therefore, we propose a method to overcome the limitation of existing signature detection method by using machine learning method for infrastructures without active patches. First, to understand the principle of meltdown, we analyze operating system driving methods such as virtual memory, memory privilege check, pipelining and guessing execution, and CPU cache. And then, we extracted data by using Linux strace tool for detecting Meltdown malware. Finally, we implemented a decision tree based dynamic detection mechanism to identify the meltdown malicious code efficiently.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.433-446
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• 2021

In this study, the future flood inundation changes under a climate change were simulated in the Tonle Sap basin in Cambodia, one of the countries with high vulnerability to climate change. For the flood inundation simulation using the rainfall-runoff-inundation (RRI) model, globally available geological data (digital elevation model [DEM]; hydrological data and maps based on Shuttle elevation derivatives [HydroSHED]; land cover: Global land cover facility-moderate resolution imaging spectroradiometer [GLCF-MODIS]), rainfall data (Asian precipitation-highly-resolved observational data integration towards evaluation [APHRODITE]), climate change scenario (HadGEM3-RA), and observational water level (Kratie, Koh Khel, Neak Luong st.) were constructed. The future runoff from the Kratie station, the upper boundary condition of the RRI model, was constructed to be predicted using the long short-term memory (LSTM) model. Based on the results predicted by the LSTM model, a total of 4 cases were selected (representative concentration pathway [RCP] 4.5: 2035, 2075; RCP 8.5: 2051, 2072) with the largest annual average runoff by period and scenario. The results of the analysis of the future flood inundation in the Tonle Sap basin were compared with the results of previous studies. Unlike in the past, when the change in the depth of inundation changed to a range of about 1 to 10 meters during the 1997 - 2005 period, it occurred in a range of about 5 to 9 meters during the future period. The results show that in the future RCP 4.5 and 8.5 scenarios, the variability of discharge is reduced compared to the past and that climate change could change the runoff patterns of the Tonle Sap basin.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.263-272
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• 2022

As a result of climate change, the heatwave and urban heat island phenomena have become more common, and the frequency of heatwaves is expected to increase by two to six times by the year 2050. In particular, the heat sensation index felt by workers at construction sites during a heatwave is very high, and the sensation index becomes even higher if the urban heat island phenomenon is considered. The construction site environment and the situations of construction workers vulnerable to heat are not improving, and it is now imperative to respond effectively to reduce such damage. In this study, satellite imagery, land surface temperatures (LST), and long short-term memory (LSTM) were applied to analyze areas above 33 ℃, with the most vulnerable areas with increased synergistic damage from heat waves and the urban heat island phenomena then predicted. It is expected that the prediction results will ensure the safety of construction workers and will serve as the basis for a construction site early-warning system.

• Journal of Computing Science and Engineering
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• v.5 no.2
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• pp.121-130
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• 2011

Recent research indicates that transient errors will increasingly become a critical concern in microprocessor design. As embedded processors are widely used in reliability-critical or noisy environments, it is necessary to develop cost-effective fault-tolerant techniques to protect processors against transient errors. The register file is one of the critical components that can significantly affect microprocessor system reliability, since registers are typically accessed very frequently, and transient errors in registers can be easily propagated to functional units or the memory system, leading to silent data error (SDC) or system crash. This paper focuses on investigating the impact of register file soft errors on system reliability and developing cost-effective techniques to improve the register file immunity to soft errors. This paper proposes the register vulnerability factor (RVF) concept to characterize the probability that register transient errors can escape the register file and thus potentially affect system reliability. We propose an approach to compute the RVF based on register access patterns. In this paper, we also propose two compiler-directed techniques and a hybrid approach to improve register file reliability cost-effectively by lowering the RVF value. Our experiments indicate that on average, RVF can be reduced to 9.1% and 9.5% by the hyperblock-based instruction re-scheduling and the reliability-oriented register assignment respectively, which can potentially lower the reliability cost significantly, without sacrificing the register value integrity.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.1
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• pp.19-32
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• 2013

As cyber stock trading grows rapidly, stock trading using Home Trading System have been brisk recently. Home Trading System is a heavy-weight in the stock market, and the system has shown 75% and 40% market shares for KOSPI and KOSDAQ, respectively. However, since Home Trading System focuses on the convenience and the availability, it has some security problems. In this paper, we found that the authentication information in memory remains during the stock trading and we proposed its countermeasure through two-channel authentication using a mobile device such as a cell phone.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.5
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• pp.225-234
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• 2018

Control flow errors are caused by the vulnerability of memory and result in system failure. Signature-based control flow monitoring is a representative method for alleviating the problem. The method commonly consists of two routines; one routine is signature update and the other is signature verification. However, in the existing signature-based control flow monitoring, monitoring target application is tightly combined with the monitoring code, and the operation of monitoring in a single thread is the basic model. This makes the signature-based monitoring method difficult to expect performance improvement that can be taken in multi-thread and multi-core environments. In this paper, we propose a new signature-based control flow monitoring model that separates signature update and signature verification in thread level. The signature update is combined with application thread and signature verification runs on a separate monitor thread. In the proposed model, the application thread and the monitor thread are separated from each other, so that we can expect a performance improvement that can be taken in a multi-core and multi-thread environment.

• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.41-51
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• 2009

Recently, there are many researches on security to remove vulnerability which is caused by wireless communication in wireless sensor networks. To guarantee secure communication, we should basically provide key management for each node, mutual authentication and key agreement protocol between two nodes. Although many protocols are presented to supply these security services, some of them require plentiful storage memory, powerful computation and communication capacity. In this paper, we propose a lightweight and efficient authentication and key agreement protocol between two sensor nodes, which is an enhanced version of Juang's scheme. In Juang's protocol, sensor node's information used to share a secret key should be transmitted to registration center via a base station. On the contrary, since node's information in our protocol is transmitted up to only base station, the proposed scheme can decrease computation and communication cost for establishing the shared key between two nodes.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.149-149
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• 2022

Accurate quantitative evaluation of baseflow contribution to streamflow is imperative to address seasonal drought vulnerability, flood occurrence and groundwater management concerns for efficient and sustainable water resources management in watersheds. Several baseflow separation algorithms using recursive filters, graphical method and tracer or chemical balance have been developed but resulting baseflow outputs always show wide variations, thereby making it hard to determine best separation technique. Therefore, the current global shift towards implementation of artificial intelligence (AI) in water resources is employed to compare the performance of deep learning models with conventional hydrograph separation techniques to quantify baseflow contribution to streamflow of Piney River watershed, Tennessee from 2001-2021. Streamflow values are obtained from the USGS station 03602500 and modeled to generate values of Baseflow Index (BI) using Web-based Hydrograph Analysis (WHAT) model. Annual and seasonal baseflow outputs from the traditional separation techniques are compared with results of Long Short Term Memory (LSTM) and simple Gated Recurrent Unit (GRU) models. The GRU model gave optimal BFI values during the four seasons with average NSE = 0.98, KGE = 0.97, r = 0.89 and future baseflow volumes are predicted. AI offers easier and more accurate approach to groundwater management and surface runoff modeling to create effective water policy frameworks for disaster management.

• SUVANNABHUMI
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• v.16 no.1
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• pp.103-123
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• 2024

The traumatic experiences of people from peripheral islands are susceptible to mnemocide. Such erasure of memory is facilitated by "defensive and complicit forgetting," which, according to Aleida Assmann, leads to "protection of perpetrators." My paper reflects on the vulnerability of traumas from the islands to mnemocide by looking into [1] the massacre of communists and civilians on Jeju Island, South Korea in 1948 as described in Hyun-Kil Un's short story "Dead Silence" (2017; English trans.) and [2] the eviction of residents and indigenous people from Calauit Island, Philippines for the creation of a safari in 1976 as imagined in Annette A. Ferrer's "Pablo and the Zebra" (2017). In "Dead Silence," I direct the attention to how to the execution of the villagers-witnesses to the death of the communist guerillas-is a three-pronged violence: it is a transgression committed against the innocent civilians; an act of "erasing traces to cover up" the military crackdown on the island; and, by leaving the corpses out in the open, a display of impunity. In "Pablo and the Zebra," I second that both residents (i.e., humans and animals) experience post-traumatic stress because of their respective displacements; thus, the tension between them has got to stop. Curiously, while it concludes with a reconciliatory gesture between an elder and a zebra, no character demanded a reparation for their traumatic past per se. Could the latter be symptomatic of a silence that lets such violence "remain concealed for a long time"?

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.273-283
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• 2020

Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R²) was 0.98 on average, indicating that the predictive model developed was very accurate.