• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.411-418
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• 2020

In the field of information security, IDS(Intrusion Detection System) is normally classified in two different categories: signature-based IDS and anomaly-based IDS. Many studies in anomaly-based IDS have been conducted that analyze network traffic data generated in cyberspace by machine learning algorithms. In this paper, we studied pre-processing methods to overcome performance degradation problems cashed by rare classes. We experimented classification performance of a Machine Learning algorithm by reconstructing data set based on rare classes and semi rare classes. After reconstructing data into three different sets, wrapper and filter feature selection methods are applied continuously. Each data set is regularized by a quantile scaler. Depp neural network model is used for learning and validation. The evaluation results are compared by true positive values and false negative values. We acquired improved classification performances on all of three data sets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.757-765
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• 2021

To carry out multi-domain operations included in the U.S. Department of Defense's national security strategy in 2018, timely data sharing between C4I systems is critical. Several studies of the Korean military have also raised the problems of interface and standardization between C4I systems, and it is necessary to establish a new plan to solve this problem. In this study, a solution to the problem was derived through case analysis that the U.S. Department of Defense has been pursuing for about 20 years to implement the data strategy after establishing the data strategy in 2003. and by establishing a data strategy suitable for the ROK military C4I system operating environment, developing a data model, selecting a standard for data sharing, and proposing a shared data development procedure, we intend to improve the data sharing capability between ROK military C4I systems.

• Journal of the Korean Applied Science and Technology
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• v.38 no.3
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• pp.689-699
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• 2021

The spread of the COVID-19 pandemic has been increasing non-face-to-face activities; as a result, this has resulted in the number of individuals obtaining medical information from the websites and mobile contents of medical institutions increasing. The study conducted the structural equation modeling to test hypotheses; as a result, all sub-factors of e-service quality of small medical clinic websites and mobile contents, usability, security, responsiveness, design, and information, had a significant positive effect on relationship quality, and relationship quality had a significant positive effect on e-Loyalty. Moreover, the structural equation model showed a good model fit, χ²/df of 2.021, NFI of .954, TLI of .969, CFI of .976, RMSEA of .046. Future research is suggested to study relationship quality by developing a system able to quickly and accurately respond to websites and mobile contents users; furthermore, improving e-service quality and relationship quality is likely to strengthen e-loyalty.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1187-1192
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• 2020

The dronebot combat system is a representative model of the future battlefield in the 4th industrial revolution. In dronebot, unmanned reconnaissance tankrobot can minimize human damage and reduce cost with higher combat power than humans. However, since the battlefield environment is very complex such as obstacles and enemy situations, it is also necessary for the pilot to control the tankrobot. Tankrobot are robots with new ICT technology, capable of hacking attacks, and if there is an abnormality in control, it can pose a threat to manipulation and control. A Bluetooth sniffing attack was performed on the communication section of the tankrobot and the controller to introduce a vulnerability to Bluetooth, and a countermeasure using MAC address exposure prevention and communication section encryption was proposed as a security measure. This paper first presented the vulnerability of tankrobot to be operated in future military operations, and will be the basic data that can be used for defense dronebot units.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.165-171
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• 2022

In this paper, we suggest the Measures to improve the Act Bill for establishing the Game User Committee. The Act Bill has a lot of problems which are violations of criminal legalism due to unclear terms in administrative punishment and violations of The Human Right enjoying freedom of occupation and guaranting property due to not defining provisisons about The Duty of Confidentiality or The Legal Fiction as Public Officials for Purposes of Applying Penalty Provisions. also the duplicate regulations in the Act Bill disrupt game industry development. we have three results that were derived through analysis of Prior studies and precedents. The First is to define details of special reasons in enforcement ordinance and enforcement regulations. The Second is to define The Duty of Confidentiality or The Legal Fiction as Public Officials for Purposes of Applying Penalty Provisions in the act bill. The Third is to address managing the random reward items in the Game Rating and Administration Committee or is to give game user advance notice about the Comntent Dispute Mediation system.

• Journal of Information Technology Services
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• v.21 no.1
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• pp.1-19
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• 2022

With the revision of the Credit Information Use and Protection Act in August 2020, the MyData service based on open banking policy will take effect in January 2022. Nonetheless, the previous studies focused on the legal system or security-related issues of such service. Therefore, this paper conducted an empirical study on financial consumers aged 20 or older nationwide to analyze the factors which influence the intention to use MyData services based on open banking. Five characteristics representing open banking-based MyData service were derived through prior research, and a research model that combined value-based adoption model and privacy calculus theory was presented. The proposed research model and the relationship of its variables was analyzed using a sample of 400 users that is randomly selected. The results of empirical analysis showed that personalization had the greatest influence on benefits and reliability on sacrifice among service characteristics. They also suggested that MyData operators should devote themselves to providing customized services optimized for customers and establishing trust relationships. It was confirmed that both usefulness and enjoyment had a great influence on perceived value, and in terms of sacrifice, the burden of financial costs had a greater influence than privacy concerns. This study is meaningful in that it explored the psychological propensity of financial consumers to identify service utilization factors and presented a new approach that can contribute to the successful settlement of the domestic MyData industry.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.319-328
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• 2021

Purpose: Recently, a large social disaster has called for the need to diagnose social disaster safety, and the Ministry of Public Administration and Security calculates and publishes regional safety ratings such as regional safety index and national safety diagnosis every year. The existing safety diagnosis system uses equal intervals or normal distribution to grade risk maps in a uniform manner. Method: However, the equidistant technique can objectively analyze risk ratings, but there is a limit to classifying risk ratings when the distribution is skewed to one side, and the z-score technique has a problem of losing credibility if the population does not follow a normal distribution. Because the distribution of statistical data varies from indicator to indicator, the most appropriate rating should be applied for each data distribution. Result: Therefore, in this paper, we analyze the data of disaster indicators and present a comparison and suitable method for traditional equidistant and natural brake techniques to proceed with optimized grading for each indicator. Conclusion: As a result, three of the six new indicators were applied differently from conventional grading techniques

• KNOM Review
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• v.24 no.2
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• pp.24-34
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• 2021

Service providers using edge computing provide a high level of service. As a result, devices store important information in inner storage and have become a target of the latest cyberattacks, which are more difficult to detect. Although experts use a security system such as intrusion detection systems, the existing intrusion systems have low detection accuracy. Therefore, in this paper, we proposed a machine learning model for more accurate intrusion detections of devices in edge computing. The proposed model is a hybrid model that combines a stacked sparse autoencoder (SSAE) and a convolutional neural network (CNN) to extract important feature vectors from the input data using sparsity constraints. To find the optimal model, we compared and analyzed the performance as adjusting the sparsity coefficient of SSAE. As a result, the model showed the highest accuracy as a 96.9% using the sparsity constraints. Therefore, the model showed the highest performance when model trains only important features.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1410-1419
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• 2023

Public perception plays a crucial role in the successful completion of a nuclear power project. As a newcomer country to nuclear power, there are lots of misconceptions among the Bangladeshi people about nuclear energy. Consequently, it is crucial to minimize all the doubts among mass people and build up their positive outlook toward nuclear power. This demands a comprehensive survey to figure out the public opinion, concerns, false impressions, and knowledge gap regarding nuclear power. In the present study, these issues were addressed by a survey that was responded to by 661 persons for the 24 survey questions. The questions were categorized based on information, knowledge, faith, benefit, awareness, and technology. Feedback and responders' basic demographic and socioeconomic information were collected from various locations in Bangladesh through online and in-person surveys. The responses were analyzed in both statistical and descriptive ways. Some of the feedback was found to vary with age, sex, and education level while others were quite independent of these parameters. It is found that socioeconomic development and energy security can be achieved by the inclusion of nuclear energy in the power system master plan of the country. However, huge knowledge gaps and misconceptions were found among the public regarding nuclear energy. As per feedback, political instability and corruption may affect the national nuclear power project in Bangladesh. Low faith in the existing rules & regulations for nuclear power programs was also observed. The result of this study will be handy to develop the communication and public awareness strategy for a successful nuclear power project in Bangladesh.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1310-1338
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• 2023

As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3- layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.