• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.143-153
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• 2022

With the development of networks and the increase in the number of network devices, the number of cyber attacks targeting them is also increasing. Since these cyber-attacks aim to steal important information and destroy systems, it is necessary to minimize social and economic damage through early detection and rapid response. Many studies using machine learning (ML) and artificial intelligence (AI) have been conducted, among which payload learning is one of the most intuitive and effective methods to detect malicious behavior. In this study, we propose a preprocessing method to maximize the performance of the model when learning the payload in term units. The proposed method constructs a high-quality learning data set by eliminating unnecessary noise (stopwords) and preserving important features in consideration of the machine language and natural language characteristics of the packet payload. Our method consists of three steps: Preserving significant special characters, Generating a stopword list, and Class label refinement. By processing packets of various and complex structures based on these three processes, it is possible to make high-quality training data that can be helpful to build high-performance ML/AI models for security monitoring. We prove the effectiveness of the proposed method by comparing the performance of the AI model to which the proposed method is applied and not. Forthermore, by evaluating the performance of the AI model applied proposed method in the real-world Security Operating Center (SOC) environment with live network traffic, we demonstrate the applicability of the our method to the real environment.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.121-128
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• 2019

Computing systems have various vulnerabilities to cyber attacks. In particular, various cyber attacks that are intelligent in the information society have caused serious social problems and economic losses. Traditional security systems are based on misuse-based technology, which requires the continuous updating of new attack patterns and the real-time analysis of vast amounts of data generated by numerous security devices in order to accurately detect. However, traditional security systems are unable to respond through detection and analysis in real time, which can delay the recognition of intrusions and cause a lot of damage. Therefore, there is a need for a new security system that can quickly detect, analyze, and predict the ever-increasing cyber security threats based on machine learning and big data analysis models. In this paper, we present a IDS model that combines machine learning and big data technology.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.

• Journal of Dairy Science and Biotechnology
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• v.41 no.3
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• pp.103-112
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• 2023

This study aimed to assess the effectiveness of 10 different pre-enrichment methods using Real-Time polymerase chain reaction (PCR) in support of the FDA method. When the initial Cronobacter spp. (Enterobacter sakazakii) inoculation was 7.2 CFU/g, the Ct values were observed in the following order: 21.37 (Enterobacteriaceae enrichment [EE] broth), 21.95 (brain heart infusion [BHI]), 22.72 (tryptic soy broth [TSB]), 23.02 (violet red bile lactose [VRBL]), 22.31 (TSB-0.1% sodium pyruvate [SP]), 23.43 (distilled water [DW]), 24.34 (phosphate buffered saline [PBS]), 24.95 (nutrient broth [NB]), 25.82 (TSB-0.6% yeast extract [YE]), and 28.27 (violet red bile glucose [VRBG]). For an inoculation of 1.82% CFU/g of Cronobacter spp. (E. sakazakii), the Ct values were recorded in this sequence: 20.34 (EE broth), 22.16 (TSB-0.6% YE), 22.37 (BHI), 22.71 (VRBL), 22.88 (TSB), 23.01 (DW), 23.19 (NB), 23.79 (TSB-0.1% SP), 24.66 (VRBG), and 24.70 (PBS). Finally, when the inoculum of Cronobacter spp. (E. sakazakii) was 0.182 CFU/g, the Ct values followed this order: 21.93 (VRBL), 23.07 (TSB-0.6% YE), 23.31 (DW), 23.47 (PBS), 23.70 (BHI), 24.14 (TSB-0.1% SP), 25.14 (TSB), 29.00 (VRBG), 31.55 (EE broth), and were undetected in the case of NB. Consequently, these results indicate that there were no significant differences among the 10 different pre-enrichment broths. Future studies should focus on exploring pre-enrichment broths that can improve the limit of detection at very low Cronobacter spp. (E. sakazakii) concentrations and enhance the selective recovery of Cronobacter spp. (E. sakazakii) under acid, antibiotic, cold, and heat damage conditions.

• The Plant Pathology Journal
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• v.40 no.3
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• pp.310-321
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• 2024

Tomato yellow leaf curl virus (TYLCV) and tomato spotted wilt virus (TSWV) are well-known examples of the begomovirus and orthotospovirus genera, respectively. These viruses cause significant economic damage to tomato crops worldwide. Weeds play an important role in the ongoing presence and spread of several plant viruses, such as TYLCV and TSWV, and are recognized as reservoirs for these infections. This work applies a comprehensive approach, encompassing field surveys and molecular techniques, to acquire an in-depth understanding of the interactions between viruses and their weed hosts. A total of 60 tomato samples exhibiting typical symptoms of TYLCV and TSWV were collected from a tomato greenhouse farm in Nonsan, South Korea. In addition, 130 samples of 16 different weed species in the immediate surroundings of the greenhouse were collected for viral detection. PCR and reverse transcription-PCR methodologies and specific primers for TYLCV and TSWV were used, which showed that 15 tomato samples were coinfected by both viruses. Interestingly, both viruses were also detected in perennial weeds, such as Rumex crispus, which highlights their function as viral reservoirs. Our study provides significant insights into the co-occurrence of TYLCV and TSWV in weed reservoirs, and their subsequent transmission under tomato greenhouse conditions. This project builds long-term strategies for integrated pest management to prevent and manage simultaneous virus outbreaks, known as twindemics, in agricultural systems.

• Research in Plant Disease
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• v.29 no.4
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• pp.433-439
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• 2023

Bacterial leaf blight in carrot is one of the most important diseases in the worldwide. In the past decade, its introduction into Korea is causing great concern due to the potential damage to carrot crops domestically. This bacterial disease is caused by Xanthomonas hortorum pv. carotae (Xhc). This study aimed to isolate and identify bacterial strains from the soil of carrot farms in Jeju Island. The bacterial isolates showing characteristics similar with those of Xhc were selected when cultured on artificial media. Through DNA sequencing and analysis based on NCBI data, some of the selected bacterial strains were identified as Xhc. Furthermore, the bacterial strains caused the typical symptom of bacterial leaf blight after inoculation on carrot leaves. The results of this study showed the potential establishment of Xhc in the soil of Jeju Island and it may be valuable data for establish a strategy preventing the domestic spread of carrot bacterial leaf blight in the future.

• Journal of the Korean Institute of Gas
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• v.28 no.1
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• pp.53-58
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• 2024

Due to the development of industry and improvement of living standards, the amount of natural gas used in the world is constantly increasing, and related industrial facilities such as power plants, storage facilities, and supply pipelines are constantly increasing. Natural gas is a convenient and clean fuel that does not pollute the environment, but in the event of an accident due to leakage, it can cause human casualties, large-scale property damage, and negative effects on the global warming effect. In addition to the severe penalties under the Severe Disaster Punishment Act, it is necessary to ensure safety. Therefore, by applying the principle of laser-based absorption spectroscopy, we developed a long-range portable methane leakage gas detection system that can detect the concentration of methane leaking from a distance of up to 30 meters and verified its effectiveness.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.4
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• pp.374-381
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• 2024

This paper delves into the application of the short-wave infrared (SWIR) region, with a focus on the synthesis and optical characteristics of silver sulfide (Ag₂S) nanostructures. SWIR offers advantages such as reduced damage to biological tissues and enhanced optical transparency, making it valuable across various domains. The study introduces three distinct synthesis methods, each showcasing the ability to obtain nanostructures with improved optical properties. These research findings open up the possibility of providing tailored solutions in detection, imaging, and other applications by controlling the size and ligands of Ag₂S nanoparticles. This paper provides new insights into the utilization of Ag₂S in the SWIR region, which is expected to foster advancements in future technologies.

• Computers and Concrete
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• v.33 no.4
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• pp.399-407
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• 2024

This study nondestructively examined the evolution of crack density in ultra-high performance concrete (UHPC) upon cyclic loading. Uniaxial compression was repeatedly applied to the cylindrical specimens at levels corresponding to 32% and 53% of the maximum load-bearing capacity, each at a steady strain rate. At each stage, both P-wave and S-wave velocities were measured in the absence of the applied load. In particular, the continuous monitoring of P-wave velocity from the first loading prior to the second loading allowed real-time observation of the strengthening effect during loading and the recovery effect afterwards. Increasing the number of cycles resulted in the reduction of both elastic wave velocities and Young's modulus, along with a slight rise in Poisson's ratio in both tested cases. The computed crack density showed a monotonically increasing trend with repeated loading, more significant at 53% than at 32% loading. Furthermore, the spatial distribution of the crack density along the height was achieved, validating the directional dependency of microcracking development. This study demonstrated the capability of the crack density to capture the evolution of microcracks in UHPC under cyclic loading condition, as an early-stage damage indicator.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.468-473
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• 2024

Excavation work in urban areas can induce ground deformation, which may damage nearby infrastructure. Such ground deformation can result in displacement of paving blocks near the construction site. Accurate measurement of these displacements can serve as an indicator for assessing the potential risks associated with ground deformation. This paper proposes a robust and efficient method for paving block displacement measurement using a 3D laser line sensor mounted on an Unmanned Ground Vehicle (UGV). The proposed method consists of two stages: 2D projection based object detection and measurement through the CPLF algorithm. Experimental results demonstrate that the CPLF algorithm is more efficient compared to the PLF algorithm, achieving an error of 1.36 mm and a processing time of 10.76 ms, confirming that the proposed method ensures robust online measurements with high accuracy in real-world environments with various types of paving blocks and environmental factors using a 3D laser line sensor on a UGV.