• Journal of Korean Society of Forest Science
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• v.108 no.2
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• pp.208-215
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• 2019

The goal of this study was to analyze the reactivity of a volumetric water content sensor (soil moisture sensor) and tensiometer and to review their use in the early detection of a shallow landslide. We attempted to demonstrate shallow and rapid slope collapses using three different soil ratios under artificial rainfall at 120 mm/h. Our results showed that the measured value of the volumetric water-content sensor converged to 30~37%, and that of the tensiometer reached -3~-5 kPa immediately before the collapse of the soil under all three conditions. Based on these results, we discussed a temporal range for early warnings of landslides using measurements of the volumetric water content sensors installed at the bottom of the soil slope, but could not generalize and clarify the exact timing for these early warnings. Further experiments under various conditions are needed to determine how to use both sensors for the early detection of shallow landslides.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.42.1-42.1
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• 2019

We observed 80 dense cores ($N(H_2)$ > $10^{22}cm^{-2}$) in the Orion molecular cloud complex which contains the Orion A (39 cores), B (26 cores), and ${\lambda}$ Orionis (15 cores) clouds. We investigate the behavior of the different molecular tracers and look for chemical variations of cores in the three clouds in order to systematically investigate the effects of stellar feedback. The most commonly detected molecular lines (with the detection rates higher than 50%) are $N_2H^+$, $HCO^+$, $H^{13}CO^+$, $C_2H$, HCN, and $H_2CO$. The detection rates of dense gas tracers, $N_2H^+$, $HCO^+$, $H^{13}CO^+$, and $C_2H$ show the lowest values in the ${\lambda}$ Orionis cloud. We find differences in the D/H ratio of $H_2CO$ and the $N_2H^+/HCO^+$ abundance ratios among the three clouds. Eight starless cores in the Orion A and B clouds exhibit high deuterium fractionations, larger than 0.10, while in the ${\lambda}$ Orionis cloud, no cores reveal the high ratio. These chemical properties could support that cores in the ${\lambda}$ Orionis cloud are affected by the photo-dissociation and external heating from the nearby H II region. An unexpected trend was found in the $[N_2H^+]/[HCO^+]$ ratio with a higher median value in the ${\lambda}$ Orionis cloud than in the Orion A/B clouds than; typically, the $[N_2H^+]/[HCO^+]$ ratio is lower in higher temperatures and lower column densities. This could be explained by a longer timescale in the prestellar stage in the ${\lambda}$ Orionis cloud, resulting in more abundant nitrogen-bearing molecules. In addition to these chemical differences, the kinematical difference was also found among the three clouds; the blue excess, which is an infall signature found in optically thick line profiles, is 0 in the ${\lambda}$ Orionis cloud while it is 0.11 and 0.16 in the Orion A and B clouds, respectively. This result could be another evidence of the negative feedback of active current star formation to the next generation of star formation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.37-47
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• 2011

In the recent years, malicious codes called malware are having shown significant increase due to the code obfuscation to evade detection mechanisms. When the code obfuscation technique is applied to malwares, they can change their instruction sequence and also even their signature. These malwares which have same functionality and different appearance are able to evade signature-based AV products. Thus, AV venders paid large amount of cost to analyze and classify malware for generating the new signature. In this paper, we propose a novel approach for detecting metamorphic malwares. The proposed mechanism first converts malware's API call sequences to call graph through dynamic analysis. After that, the callgraph is converted to semantic signature using 128 abstract nodes. Finally, we extract all subgraphs and analyze how similar two malware's behaviors are through subgraph similarity. To validate proposed mechanism, we use 273 real-world malwares include obfuscated malware and analyze 10,100 comparison results. In the evaluation, all metamorphic malwares are classified correctly, and similar module behaviors among different malwares are also discovered.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.531-540
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• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.25-36
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• 2023

Today, as AI (Artificial Intelligence) technology is introduced in various fields, including security, the development of technology is accelerating. However, with the development of AI technology, attack techniques that cleverly bypass malicious behavior detection are also developing. In the classification process of AI models, an Adversarial attack has emerged that induces misclassification and a decrease in reliability through fine adjustment of input values. The attacks that will appear in the future are not new attacks created by an attacker but rather a method of avoiding the detection system by slightly modifying existing attacks, such as Adversarial attacks. Developing a robust model that can respond to these malware variants is necessary. In this paper, we propose two methods of generating Adversarial attacks as efficient Adversarial attack generation techniques for improving Robustness in AI models. The proposed technique is the XAI-based attack technique using the XAI technique and the Reference based attack through the model's decision boundary search. After that, a classification model was constructed through a malicious code dataset to compare performance with the PGD attack, one of the existing Adversarial attacks. In terms of generation speed, XAI-based attack, and reference-based attack take 0.35 seconds and 0.47 seconds, respectively, compared to the existing PGD attack, which takes 20 minutes, showing a very high speed, especially in the case of reference-based attack, 97.7%, which is higher than the existing PGD attack's generation rate of 75.5%. Therefore, the proposed technique enables more efficient Adversarial attacks and is expected to contribute to research to build a robust AI model in the future.

• Information Systems Review
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• v.23 no.4
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• pp.45-67
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• 2021

As the transaction volume of the C2C second-hand market is growing, the number of frauds, which intend to earn unfair gains by sending products different from specified ones or not sending them to buyers, is also increasing. This study explores the model that can identify frauds in the online C2C second-hand market by examining the postings for transactions. For this goal, this study collected 145,536 field data from actual C2C second-hand market. Then, the model is built with the characteristics from postings such as the topic and the linguistic characteristics of the product description, and the characteristics of products, postings, sellers, and transactions. The constructed model is then trained by the machine learning algorithm XGBoost. The final analysis results show that fraudulent postings have less information, which is also less specific, fewer nouns and images, a higher ratio of the number and white space, and a shorter length than genuine postings do. Also, while the genuine postings are focused on the product information for nouns, delivery information for verbs, and actions for adjectives, the fraudulent postings did not show those characteristics. This study shows that the various features can be extracted from postings written in C2C second-hand transactions and be used to construct an effective model for frauds. The proposed model can be also considered and applied for the other C2C platforms. Overall, the model proposed in this study can be expected to have positive effects on suppressing and preventing fraudulent behavior in online C2C markets.

• Korean Journal of Environmental Biology
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• v.26 no.4
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• pp.330-342
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• 2008

A comprehensive quality survey for PCDDs/PCDFs and coplanar PCBs as well as heavy metals (Cu, Zn, Cd and Pb) in sediments has been investigated in August 2006, Korea. Monitoring was undertaken at five streams representing different surrounding environments throughout Juwang and Gapyeong streams (reference sites), Jungrang stream (dense population site), Ansan stream (mixed small population and industrial site), and Siheung stream (heavy industrial site). The levels of heavy metal in samples were found to be significantly higher in sediment from Siheung stream compared to those of other stream sites. The heavy metal concentrations (dry weight basis) in sediment from Siheung stream were as follows; Cd (3.7 ${\mu}g$/g), Pb (1,295 ${\mu}g$/g), Cu (713.4 ${\mu}g$/g) and Zn (358.1 ${\mu}g$/g). Among 12 coplanar PCBs and 17 PCDDs/PCDFs selected as target compounds in this study, PCB (IUPAC no. 118) and OCDD were the most abundant congeners found in all sediment samples, followed by 1,2,3,4,6,7,8-HpCDD, OCDF and 1,2,3,4,6,7,8HpCDF as well as PCB (IUPAC no. 105). These results were shown to be in the same trend as the sediment samples of other countries. The levels of PCDDs/PCDFs/coplanar PCBs in sediment samples were expressed as concentrations and WHO- TEQ values. The PCDDs/PCDFs/coplanar PCBs concentrations and their WHO-TEQ values in sediment from Siheung stream were remarkably high. The levels detected were 788.16 pg/g and 36.080 pg WHO-TEQ/g dry weight for PCDDs/ PCDFs and 314 pg/g and 0.4189 pg WHO-TEQ/g dry weight for coplanar PCBs, respectively, beyond the safety level of sediment value 20 pg WHO-TEQ/g. Sediment samples of the five streams were also monitored by sensitive biomarkers using insect immune responses: hemocyte-spreading behavior and immune-associated enzyme activities of phospholipase A$_2$ (PLA$_2$) and phenoloxidase. Organic extracts of Siheung and Jungrang sediments significantly interfered with the hemocytespreading behavior, whereas those of Ansan, Gapyeong, and Juwang did not. These organic extracts did not inhibit the PLA$_2$ and phenoloxidase activities. However, phenoloxidase was highly susceptible to exposure to aqueous extracts in all site sediments. In comparison, PLA$_2$ activities of the hemocytes were significantly inhibited only by aqueous extracts of Siheung, Jungrang, and Gapyeong sediments, but not by those of Ansan and Juwang. Despite some disparity between bioand chemical monitoring results, the biomarkers can be recommended as a device warning the contamination of biohazard environmental chemicals because of a fast and inexpensive detection method.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.8
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• pp.633-643
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• 2015

In this study, we develop a highly efficient conical-air diffuser that generates fine bubble. By inserting a sufficient number of aerotropic microorganisms with dissolved oxygen from an air diffuser and minimizing the air-channel blockages within the air diffuser, we expect to improve the efficiency and durability of the decomposition process for organic waste. To upgrade the conventional air diffuser, we perform experiments and numerical analysis to develop a conical-type that generates fine bubble, and which is free from nozzle blockage. We complement the air-diffuser design by numerically analyzing the internal air-flow pattern within the diffuser. Then, by applying the diffuser to a mockup bioreactor, we experimentally and numerically study the bubble behavior observed in the diffuser and the 2-phase fluid flow in the bioreactor. The results obtained include statistics of the cord length and increased velocity, and we investigate the mechanisms of the fluid-flow characteristics including bubble clouds. Throughout the study, we systemize the design procedures for the design of efficient air diffusers, and we visualize the fluid-flow patterns caused by bubble generation within the mockup bioreactor. These results will provide a meaningful basis for further study as well as the detection of oxygen transfer and fluid-flow characteristics in real-scale bio-reactors using sets of air diffusers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.593-596
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• 2018

The speed of vehicles has remained a significant factor that influences the severity of accidents and traffic accident rate in many parts of the world including South Korea. This behavior where drivers drive at speeds which exceed a posted safe threshold is known as 'speeding'. Over the past twenty years, the Korean National Police Agency (NPA) has become aware of an increased frequency of drivers who are speeding. Therefore, fixed-type ASE systems [1] have been installed on hazardous road sections of many highways. These system monitor vehicle speeds using a camera. However, the use of ASE systems has changed the behavior of the drivers. Specifically, drivers reduce speed or avoid the route where the cameras are mounted. It is not practical to install cameras at every possible location. Therefore, it is challenging to thoroughly explore the location where speeding occurs. In view of these problems, the author of this paper designed and implemented a prototype visualization system in which point and color are used to show vehicle location and associated over-speed information. All of this information was used to create a comprehensive visualization application to show information about vehicle driving. In this paper, we present an approach detecting vehicles moving at speeds which exceed a threshold and visualizing the points those violations occur on a map. This was done using vehicle trajectory data collected in Daegu city. We propose steps for exploring the data collected from those sensors. The resulting mapping has two layers. The first layer contains the dynamic vehicle trajectory data. The second underlying layer contains the static road networks. This allows comparing the speed of vehicles on roads with the known maximum safe speed of those roads, and presents the results with a visualization tool. We also compared data about people who drive over threshold safe speeds on each road on days and weekends based on vehicle trajectories. Finally, our study suggests improved times and locations where law enforcement should use monitoring with speed cameras, and where they should be stricter with traffic law enforcement. We learned that people will drive over the speed limit at midnight more than 1.9 times as often when compared with rush hour traffic at 8 o'clock in the morning, and 4.5 times as often when compared with traffic at 7 o'clock in the evening. Our study can benefit the government by helping them select better locations for installation of speed cameras. This would ultimately reduce police labor in traffic speed enforcement, and also has the potential to improve traffic safety in Daegu city.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3333-3343
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• 2015

The purpose of this study is to investigate factors related to substance use focusing on the sexual experiences and risky sexual behaviors among Korean adolescents. This study conducted with a secondary data analysis using the Seventh Korea Youth Risk Behavior Web-based Survey 2011 in South Korea. The study participants selected 75,643 Korean adolescents who were recruited anonymously through national web-based survey. Data were analyzed using descriptive statistics, rao-Scott ${\chi}^2$, and logistic regressions considering complex samples. Results indicated that substance use was significantly associated with sexual experiences such as sexual intercourse with the opposite sex (adjusted OR=5.691; 95% CI=4.223-7.670), sexual intercourse with the same sex (adjusted OR=21.180; 95% CI=14.704-30.507) and risky sexual behaviors such as sexual assaults (adjusted OR=9.936; 95% CI=7.217-13.680), date rape (adjusted OR=16.979; 95% CI=11.648-24.751), sexual intercourse after drinking (adjusted OR=6.564; 95% CI=3.986-10.810), unexpected pregnancies (adjusted OR=18.628; 95% CI=8.476-40.936), and sexually transmitted diseases (adjusted OR=21.872; 95% CI=13.920-34.367) in Korean adolescents. Findings of this study show that adolescents who use substances are more likely to engage in risky sexual behaviors. Therefore, intervention program should be designed to consider both substance use and sexual problems and to include the effective approaches at the family and school level.