• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.3
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• pp.53-65
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• 2010

Conditional Access System (CAS) is a core security mechanism of IPTV SCP (Service and Content Protection) which enables only authenticated user to be able to watch the broadcasting contents. In the past, it was general that CAS was built in Set-Top Box (STB) as hardware or as a detachable cable card. However, numerous researches in Downloadable CAS (DCAS), where users can download CAS code in their STB through their network, have been recently conducted widely due to the lack of security and scalability problem. In this paper, the security requirements of OpenCable based DCAS which is typical example of downloadable IPTV SCP will be derived, the novel authentication and key management scheme will be proposed by using the Authentication Proxy (AP) which is the core DCAS. Also, the benefits of the proposed system will be evaluated by comparison and analysis with preceding research.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.67-81
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• 2010

As wide propagation of smartphones, e-commerce with smartphones increases rapidly. Such as transfer or stock trade systems. It has prospect that most of financial companies going to offer e-commerce systems via smartphones. And e-commerce via smartphones will be increased, hence the nature of smartphone that can be used whenever, wherever. However, legislation of e-commerce in Korea does not reflect these characteristics of smartphones, because it has set standards in regular PC. So that this study is security threat and feature of smartphones considering that the current legal system will use Certificate constraints, ensuring the safety of e-commerce and install security programs for protection of users, e-commerce responsible for the accident analysis has focused on the issues presented for this improvement.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.5
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• pp.37-48
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• 2010

Designated verifier signatures allow a signer to prove the validity of a signature to a specifically designated verifier. The designated verifier can be convinced but unable to prove the source of the message to a third party. Unlike conventional digital signatures, designated verifier signatures make it possible for a signer to repudiate his/her signature against anyone except the designated verifier. Recently, two designated verifier signature schemes, Zhang et al.'s scheme and Kang et al.'s scheme, have been shown to be insecure by concrete attacks. In this paper, we find the essential reason that the schemes open attacks while those were given with its security proofs, and show that Huang-Chou scheme and Du-Wen scheme have the same problem. Indeed, the security proofs of all the schemes reflect no message attackers only. Next, we show that Huang-Chou scheme is insecure by presenting universal forgery attack. Finally, we show that Du-Wen scheme is, indeed, secure by completing its defective security proof.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.103-116
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• 2021

Analysis of runoff is substantial for effective water management in the watershed. Runoff occurs by reaction of a watershed to the rainfall and has non-linearity and uncertainty due to the complex relation of weather and watershed factors. ANN (Artificial Neural Network), which learns from the data, is one of the machine learning technique known as a proper model to interpret non-linear data. The performance of ANN is affected by the ANN's structure, the number of hidden layer nodes, learning rate, and activation function. Especially, the activation function has a role to deliver the information entered and decides the way of making output. Therefore, It is important to apply appropriate activation functions according to the problem to solve. In this paper, ANN models were constructed to estimate runoff with different activation functions and each model was compared and evaluated. Sigmoid, Hyperbolic tangent, ReLU (Rectified Linear Unit), ELU (Exponential Linear Unit) functions were applied to the hidden layer, and Identity, ReLU, Softplus functions applied to the output layer. The statistical parameters including coefficient of determination, NSE (Nash and Sutcliffe Efficiency), NSEln (modified NSE), and PBIAS (Percent BIAS) were utilized to evaluate the ANN models. From the result, applications of Hyperbolic tangent function and ELU function to the hidden layer and Identity function to the output layer show competent performance rather than other functions which demonstrated the function selection in the ANN structure can affect the performance of ANN.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.494-504
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• 2021

The spread of Over-The-Top (OTT) service, which represents Netflix, and the social distancing caused by COVID-19, acted as an overall bad news for domestic multiplex movie theaters. In addition to this, the phenomenon of digital shifting was added, and the need for domestic offline movie theaters to seek a new market for growth emerged. This study focused on the concept of consumer value perception amid this problem consciousness, and attempted to investigate the relationship between the brand image of multiplex movie theaters and purchase satisfaction according to the type of consumer value perception. After data was sampled through a questionnaire survey to a total of 350 subjects, the results of empirical analysis according to the study model are as follows. Among the types of value perception of offline movie viewers, practicality had the strongest influence on brand image construction, and self-faithfulness had the strongest influence on purchase satisfaction of offline movie watching. In addition, the brand image of offline movie theaters had a positive(+) effect on the purchase satisfaction of moviegoers. Based on this, this study suggested a new survival strategy in the new normal era of offline Multiplex Cinemas.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.251-258
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• 2021

In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.

• Journal of Korean Academy of Nursing
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• v.51 no.3
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• pp.265-279
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• 2021

Purpose: This study examined the impact of a drinking reduction program on drinking motivation, drinking refusal self-efficacy, and problematic drinking behaviors in college students with problematic drinking habits. Methods: This study incorporated a non-equivalent control group prepost-test design. Study participants included 58 college students who scored 12 or more in the AUDIT-K test (Alcohol Use Disorders Identification Test-Korean version) (experimental group: 30; control group: 28). The intervention consisted of eight sessions and was conducted once a week. It was designed to promote autonomy, competence, and relatedness-the three elements of basic psychological needs in self-determination theory. The participants were assessed before the intervention, immediately after, and four weeks post intervention. Data were collected from October 12 to December 31, 2017. The analysis employed the chi-square test, Fisher's exact test, independent t-test, and repeated measures ANOVA using SPSS/WIN 22.0. Results: The mean age of participants was 21.8 years. There were 30 men (51.7%) and 28 women (48.3%). The differences in drinking motivation, drinking refusal self-efficacy, and problematic drinking behaviors were statistically significant for the group by time interaction (F = 42.56, p < .001; F = 54.96, p < .001; F = 39.90, p < .001, respectively). Conclusion: The findings indicate that the intervention effectively decreases drinking motivation, increases drinking refusal self-efficacy, and decreases problematic drinking behaviors. It can be an efficient strategy for college students with problematic drinking habits to enhance their self-determination ability.

• Korean Journal of Materials Research
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• v.31 no.7
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• pp.392-396
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• 2021

Expensive PCBN or ceramic cutting tools are used for processing of difficult-to-cut materials such as Ti and Ni alloy materials. These tools have the problem of breaking easily due to their high hardness but low fracture toughness. To solve these problems, cutting tools that form various coating layers are used in low-cost WC-Co hard material tools, and research on various tool materials is being conducted. In this study, binderless-WC, WC-6 wt%Co, WC-6 wt%Co-1 wt% Mo₂C, and WC-6 wt%Co-2.5 wt% Mo₂C hard materials are densified using horizontal ball milled WC-Co, WC-Co-Mo₂C powders, and spark plasma sintering process (SPS process). Each SPSed Binderless-WC, WC-6 wt%Co-1 wt% Mo₂C, and WC-6 wt%Co-2.5 wt% Mo₂C hard materials are almost completely dense, with relative density of up to 99.5 % after the simultaneous application of pressure of 60 MPa and almost no significant change in grain size. The average grain sizes of WC for Binderless-WC, WC-6 wt%Co-1 wt% Mo₂C, and WC-6 wt%Co-2.5 wt% Mo₂C hard materials are about 0.37, 0.6, 0.54, and 0.43 ㎛, respectively. Mechanical properties, microstructure, and phase analysis of SPSed Binderless-WC, WC-6 wt%Co-1 wt% Mo₂C, and WC-6 wt%Co-2.5 wt% Mo₂C hard materials are investigated.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.45-52
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• 2021

Although many studies have been conducted to identify unknown attacks in cyber security intrusion detection systems, studies based on outliers are attracting attention. Accordingly, we identify outliers by defining categories for unknown attacks. The unknown attacks were investigated in two categories: first, there are factors that generate variant attacks, and second, studies that classify them into new types. We have conducted outlier studies that can identify similar data, such as variants, in the category of studies that generate variant attacks. The big problem of identifying anomalies in the intrusion detection system is that normal and aggressive behavior share the same space. For this, we applied a technique that can be divided into clear types for normal and attack by discrete wavelet transformation and detected anomalies. As a result, we confirmed that the outliers can be identified through One-Class SVM in the data reconstructed by discrete wavelet transform.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.47-55
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• 2019

As the damage caused by GPS jamming has been increased, researches for detecting and preventing GPS jamming is being actively studied. This paper deals with a GPS jamming detection method using multiple GPS receiving channels and three-types machine learning techniques. Proposed multiple GPS channels consist of commercial GPS receiver with no anti-jamming function, receiver with just anti-noise jamming function and receiver with anti-noise and anti-spoofing jamming function. This system enables user to identify the characteristics of the jamming signals by comparing the coordinates received at each receiver. In this paper, The five types of jamming signals with different signal characteristics were entered to the system and three kinds of machine learning methods(AB: Adaptive Boosting, SVM: Support Vector Machine, DT: Decision Tree) were applied to perform jamming detection test. The results showed that the DT technique has the best performance with a detection rate of 96.9% when the single machine learning technique was applied. And it is confirmed that DT technique is more effective for GPS jamming detection than the binary classifier techniques because it has low ambiguity and simple hardware. It was also confirmed that SVM could be used only if additional solutions to ambiguity problem are applied.