• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.77-82
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• 2023

The article considers online discussion as an interactive learning method in the conditions of distance learning. The essence of discussion and the stages of its organization are described. The main objective of discussion in distance learning is defined as the stimulation of interest in learning and the involvement of various viewpoints in an active discussion of the stated problems. The key role in ensuring the efficiency of a discussion is identified. The article develops a model for organizing asynchronous online discussions on the Moodle platform, highlighting the sequence of stages and their content. An experimental study of the use of the discussion method in the training of students in distance learning conditions is carried out. Based on the results of the methodological experiment, conclusions are drawn about student interest in online discussions. The authors conclude that the interest of students of different specialties in asynchronous online discussions varies, and the greatest interest is demonstrated by linguistics students. Nevertheless, the differences in student interest in online discussions by groups (specialties) are more likely attributable to subjective factors, which do not affect the overall picture in a major way.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.85-91
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• 2024

With the current surge in leisure sports activities involving firearms and the costly shooting practices in the military, there's a growing interest in using virtual reality as a cost-effective alternative. This study proposes a system that addresses the drawbacks of existing shooting practice setups, such as dim spaces and high installation costs, by making it feasible on large display screens. The system integrates IR receivers and guns for practice, ensuring usability and efficiency through an application. Additionally, an accuracy adjustment feature enhances precise coordination recognition. As a result, this cyber light gun system offers an affordable solution for outdoor training.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.486-503
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• 2023

Logs play an important role in mastering the health of the system, experienced operation and maintenance engineer can judge which part of the system has a problem by checking the logs. In recent years, many system architectures have changed from single application to distributed application, which leads to a very huge number of logs in the system and manually check the logs to find system errors impractically. To solve the above problems, we propose a method based on Message Middleware and ATT-GRU (Attention Gate Recurrent Unit) to detect the logs anomaly of distributed systems. The works of this paper mainly include two aspects: (1) We design a high-performance distributed logs collection architecture to complete the logs collection of the distributed system. (2)We improve the existing GRU by introducing the attention mechanism to weight the key parts of the logs sequence, which can improve the training efficiency and recognition accuracy of the model to a certain extent. The results of experiments show that our method has better superiority and reliability.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.11-20
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• 2023

The primary aim of this study is to investigate the influence of Service Provider Quality (SPQ), System Quality (SQ), Information Quality (IQ), and Training Quality (TQ) on the interconnected aspect of organizational performance known as growth and development (GD). The study examined the influence of information systems (IS) on organisational performance and provided a theory-based technique for conducting research. The theoretical foundation for this study is derived from the widely employed [1]. IS success model in information systems research. The study's framework incorporates several novel elements, drawn from a comprehensive review of both recent and earlier literature, which researchers have utilized to evaluate the dimensions of [1]. In this study, we collected data from a diverse group of 348 individuals representing various industries through a web-based questionnaire. The collected data were subjected to analysis using SPSS. We conducted a multiple regression analysis involving 15 factors to assess several hypotheses regarding the relationship between the independent construct IS effectiveness and the dependent construct organizational performance. Several noteworthy descriptive statistics emerged, which hold significance for management. The study's findings strongly indicate that information systems exert a significant and beneficial influence on organizational performance. To sustain and continually enhance organizational effectiveness, the study recommends that managers periodically scrutinize and assess their information systems.

• The Journal of Korean Association of Computer Education
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• v.6 no.3
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• pp.65-73
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• 2003

Most web-based learning is furnishing single side and consistent training resource to learner. Research was gone to apply item analysis method or to introduce web-based learning system using studying pattern. But, we need complicated algorithm or parameter setting etc, for apply these method. Therefore, in this research, we design and implement an item selection system in consideration of learner's incorrectness rate and problem frequency selection rate about question of item selection attribute. Also, as that embody business logic about item selection by EJB, efficient system development is available and we improved maintenance and reusability.

• 한국정보교육학회:학술대회논문집
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• 2010.08a
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• pp.95-99
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• 2010

In this study, using the program in elementary school, scratch, based on user-centered design model, a high-level (High Level) step by applying prototyping techniques for application development, training and present a model applied to investigate reports that validate the effectiveness. The results of this study, problem solving and logical thinking ability in elementary school for the education of the new approach to application development is expected to be.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.1
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• pp.35-41
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• 2003

In this paper, a machine vision system for recognizing and classifying the patterns and marks engraved by die molding or laser marking on the glass panels of computer monitors is suggested and evaluated experimentally. The vision system is equipped with a neural network and an NGC pattern classifier including searching process based on normalized grayscale correlation and adaptive binarization. This system is found to be applicable even to the cases in which the segmentation of the pattern area from the background using ordinary blob coloring technique is quite difficult. The inspection process is accomplished by the use of the NGC hypothesis and ANN verification. The proposed pattern recognition system is composed of three parts: NGC matching process and the preprocessing unit for acquiring the best quality of binary image data, a neural network-based recognition algorithm, and the learning algorithm for the neural network. Another contribution of this paper is the method of generating the training patterns from only a few typical product samples in place of real images of all types of good products.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.928-931
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• 2002

Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.