• Journal of Digital Contents Society
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• v.13 no.3
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• pp.393-399
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• 2012

New and creative skills must be developed, and adapted into a lesson, to motivate learners to acquire a second language easily and enjoyment, Multimedia tools which are of interest to learners, such as; smart phones, computers, and notebooks with wireless internet compatability, will provide learners opportunities to study, and do their work practically anywhere and anytime. Recently, podcasts, which are a type of digital media, consisting of a series of audio episodes or video files, subscribed to and downloaded through web syndication, or streamed online to a computer or mobile device, are used to facilitate ESL (English as a Second Language) learning. Development of a variety of teaching methods, using multimedia tools, is needed. There are advantages and disadvantages to using a variety of multimedia tools. The current research aims to study its characteristics and application, in order to maximize their effective use, in English education. The current study suggests a ubiquitous learning environment using multimedia content tools, internet media, video teleconferencing, cyber-learning, and one-to-one videos used in conjunction with, or as a digital textbook for the English lesson. This study also investigates future educational changes, using state-of-the-art equipment for the self-learning experience, and will present a new direction in English education, through a variety of instructional devices and a marginalized class system model.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.29-39
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• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.

• Communications of Mathematical Education
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• v.33 no.3
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• pp.163-180
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• 2019

This study introduces a development of calculus contents which makes to understand the main concepts of calculus in a short period of time and to enhance problem solving and computational thinking for complex problems encountered in the real world for college freshmen with diverse backgrounds. As a concrete measure, we developed 'Teaching and Learning' contents and Python-based code for Calculus I and II which was used in actual classroom. In other words, the entire process of teaching and learning, action plan, and evaluation method for calculus class with Python based coding are reported and shared. In anytime and anywhere, our students were able to freely practice and effectively exercise calculus problems. By using the given code, students could gain meaningful understanding of calculus contents and were able to expand their computational thinking skills. In addition, we share a way that it motivated student activities, and evaluated students fairly based on data which they generated, but still instructor's work load is less than before. Therefore, it can be a teaching and learning model for college mathematics which shows a possibility to cover calculus concepts and computational thinking at once in a innovative way for the 21st century.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.499-510
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• 2023

Recently, an intelligent and advanced cyber attack attacks a computer network of a public institution using a file containing malicious code or leaks information, and the damage is increasing. Even in public institutions with various information protection systems, known attacks can be detected, but unknown dynamic and encryption attacks can be detected when existing signature-based or static analysis-based malware and ransomware file detection methods are used. vulnerable to The detection method proposed in this study extracts the detection result data of the system that can detect malicious code and ransomware among the information protection systems actually used by public institutions, derives various attributes by combining them, and uses a machine learning classification algorithm. Results are derived through experiments on how the derived properties are classified and which properties have a significant effect on the classification result and accuracy improvement. In the experimental results of this paper, although it is different for each algorithm when a specific attribute is included or not, the learning with a specific attribute shows an increase in accuracy, and later detects malicious code and ransomware files and abnormal behavior in the information protection system. It is expected that it can be used for property selection when creating algorithms.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.317-326
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• 2024

Biometric recognition is a technology that determines whether a person is identified by extracting information on a person's biometric and behavioral characteristics with a specific device. Cyber threats such as forgery, duplication, and hacking of biometric characteristics are increasing in the field of biometrics. In response, the security system is strengthened and complex, and it is becoming difficult for individuals to use. To this end, multiple biometric models are being studied. Existing studies have suggested feature fusion methods, but comparisons between feature fusion methods are insufficient. Therefore, in this paper, we compared and evaluated the fusion method of multiple biometric models using fingerprint, face, and iris images. VGG-16, ResNet-50, EfficientNet-B1, EfficientNet-B4, EfficientNet-B7, and Inception-v3 were used for feature extraction, and the fusion methods of 'Sensor-Level', 'Feature-Level', 'Score-Level', and 'Rank-Level' were compared and evaluated for feature fusion. As a result of the comparative evaluation, the EfficientNet-B7 model showed 98.51% accuracy and high stability in the 'Feature-Level' fusion method. However, because the EfficietnNet-B7 model is large in size, model lightweight studies are needed for biocharacteristic fusion.