• Convergence Security Journal
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• v.23 no.5
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• pp.65-72
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• 2023

This paper proposes a method for detecting malicious domains considering human habitual characteristics by building a Deep Learning model based on LSTM (Long Short-Term Memory). DGA (Domain Generation Algorithm) malicious domains exploit human habitual errors, resulting in severe security threats. The objective is to swiftly and accurately respond to changes in malicious domains and their evasion techniques through typosquatting to minimize security threats. The LSTM-based Deep Learning model automatically analyzes and categorizes generated domains as malicious or benign based on malware-specific features. As a result of evaluating the model's performance based on ROC curve and AUC accuracy, it demonstrated 99.21% superior detection accuracy. Not only can this model detect malicious domains in real-time, but it also holds potential applications across various cyber security domains. This paper proposes and explores a novel approach aimed at safeguarding users and fostering a secure cyber environment against cyber attacks.

• Convergence Security Journal
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• v.23 no.5
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• pp.209-221
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• 2023

Due to the rapid spread of the COVID-19 virus in December 2019, the working environment was rapidly converted to telecommuting. However, since the defense industry is an organization that handles technology related to the military, the network separation policy is applied, so there are many restrictions on the application of telecommuting. Telecommuting is a global change and an urgent task considering the rapidly changing environment in the future. Currently, in order for defense companies to implement telecommuting, VPN, VDI, and network interlocking systems must be applied as essential elements. Eventually, some contact points will inevitably occur, which will increase security vulnerabilities, and strong security management is important. Therefore, in this paper, attack types are selected and threats are analyzed based on the attack tactics of the MITER ATT&CK Framework, which is periodically announced by MITER in the US to systematically detect and respond to cyber attacks. Then, by applying STRIDE threat modeling, security threats are classified and specific security requirements are presented.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.17-23
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• 2024

Losses in domestic water supply due to leaks are very large, such as fractures and defects in pipelines. Therefore, preventive measures to prevent water leakage are necessary. We propose the development of a leakage detection sensor utilizing vibration sensors and present an optimal leakage detection algorithm leveraging artificial intelligence. Vibrational sound data acquired from water pipelines undergo a preprocessing stage using FFT (Fast Fourier Transform), followed by leakage classification using an optimized tree-based boosting algorithm. Applying this method to approximately 260,000 experimental data points from various real-world scenarios resulted in a 97% accuracy, a 4% improvement over existing SVM(Support Vector Machine) methods. The processing speed also increased approximately 80 times, confirming its suitability for edge device applications.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.227-239
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• 2024

This study aims to analyze the impact of vocational training received by learning workers through the degree-linked work-study program on their learning outcomes. Specifically, we explore the causal relationship between various factors considered during university degree program admission and selection, and the average GPA (Grade Point Average) after admission. To achieve this, we conducted regression analysis and variance analysis using historical admission data and GPA records of 976 students from three undergraduate programs at a domestic K university that implements the degree-linked work-study model. Additionally, we included company information from publicly available databases that could potentially influence the academic performance of learning workers. Our analysis revealed significant causal relationships across various factors, including the classification of the high school attended, gender, family background, subject-specific grades in high school, duration of employment at the company, and age at the time of admission. Based on these findings, we anticipate that universities operating similar degree programs can enhance their selection procedures for learning workers. Furthermore, the results of this study can serve as foundational data for future policy recommendations related to degree-linked work-study programs.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.76-90
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• 2024

Although artificial intelligence (AI) can be utilized in various domains such as smart city, healthcare, it is limited due to concerns about the exposure of personal and sensitive information. In response, the concept of distributed machine learning has emerged, wherein learning occurs locally before training a global model, mitigating the concentration of data on a central server. However, overall learning phase in a collaborative way among multiple participants poses threats to data privacy. In this paper, we systematically analyzes recent trends in privacy protection within the realm of distributed machine learning, considering factors such as the presence of a central server, distribution environment of the training datasets, and performance variations among participants. In particular, we focus on key distributed machine learning techniques, including horizontal federated learning, vertical federated learning, and swarm learning. We examine privacy protection mechanisms within these techniques and explores potential directions for future research.

• Environmental and Resource Economics Review
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• v.33 no.1
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• pp.87-112
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• 2024

This study estimates the regional input-output table and GHG emissions in 2019 and then analyzes the economic effects of carbon taxes by region and industry in Korea. The GHG emission, emission coefficient, and emission induction coefficient are estimated to be higher in manufacturing-oriented metropolitan provinces. The GHG emission coefficient in the same industry varies from region to region, which might reflect the standard of product classification, characteristics of production technology, and the regional differences in input structure. If a carbon tax is imposed, production costs are expected to increase and demand and production will decrease, especially in the manufacturing industry, which emits more GFG. On the other hand, the impact of carbon taxes on each region is not expected to vary significantly from region to region, which might be due to the fact that those differences are mitigated by industry-related effects. Since the impact of carbon taxes is expected to spread to the entire region, close cooperation between local governments is necessary in the process of implementing carbon neutrality in the future.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.312-318
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• 2006

The conventional lip-synch system has a two-step process, speech segmentation and recognition. However, the difficulty of speech segmentation procedure and the inaccuracy of training data set due to the segmentation lead to a significant Performance degradation in the system. To cope with that, the connected vowel recognition method using Head-Body-Tail (HBT) model is proposed. The HBT model which is appropriate for handling relatively small sized vocabulary tasks reflects co-articulation effect efficiently. Moreover the 7 vowels are merged into 3 classes having similar lip shape while the system is optimized by employing a class dependent SCHMM structure. Additionally in both end sides of each word which has large variations, 8 components Gaussian mixture model is directly used to improve the ability of representation. Though the proposed method reveals similar performance with respect to the CHMM based on the HBT structure. the number of parameters is reduced by 33.92%. This reduction makes it a computationally efficient method enabling real time operation.

• Information Systems Review
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• v.22 no.1
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• pp.113-124
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• 2020

In the operation of farms, it is very important to evaluate the quality of harvested crops and to classify defective products. However, farmers have difficulty coping with the cost and time required for quality assessment due to insufficient capital and manpower. This study thus aims to detect defects by analyzing the epidermis of fruit using deep learning algorithm. We developed a model that can analyze the epidermis by applying YOLOv3 algorithm based on Region Convolutional Neural Network to video images of peach. A total of four classes were selected and trained. Through 97,600 epochs, a high performance detection model was obtained. The crop failure detection model proposed in this study can be used to automate the process of data collection, quality evaluation through analyzed data, and defect detection. In particular, we have developed an analytical model for peach, which is the most vulnerable to external wounds among crops, so it is expected to be applicable to other crops in farming.

• Smart Media Journal
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• v.12 no.11
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• pp.113-124
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• 2023

With the advent of 5G and B5G (Beyond 5G) networks, network virtualization technology that can overcome the limitations of existing networks is attracting attention. The purpose of network virtualization is to provide solutions for efficient network resource utilization and various services. Existing heuristic-based VNE (Virtual Network Embedding) techniques have been studied, but the flexibility is limited. Therefore, in this paper, we propose a GNN-based network slicing classification scheme to meet various service requirements and a RL-based VNE scheme for optimal resource allocation. The proposed method performs optimal VNE using an Actor-Critic network. Finally, to evaluate the performance of the proposed technique, we compare it with Node Rank, MCST-VNE, and GCN-VNE techniques. Through performance analysis, it was shown that the GNN and RL-based VNE techniques are better than the existing techniques in terms of acceptance rate and resource efficiency.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.501-529
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• 2020

Because of the inherent complex anatomy and functional arrangement of the cranial nerves (CNs), neuroimaging of cranial neuropathy is challenging. With recent advances in magnetic resonance imaging (MRI) techniques, the cause of cranial neuropathy can now be detected in many cases. As an active multidisciplinary team member of cranial neuropathy, it is essential for the neuroradiologist to be familiar with the detailed anatomy of the CNs on MRI. This review contains the basic MRI anatomy of CNs III-XII according to a segmental classification from the brain stem to the extracranial region. The optimal imaging options to best evaluate the specific segment of the CNs will also be discussed briefly.