• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.11
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• pp.2043-2047
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• 2010

This study is to solve problems of depletion of RF bandwidth frequency, confusion possibility, security that current wireless communications system have and is to confirm possibility of applying next generation network. To solve problems of current wireless communications system, visible light communications system for power line communications using ATmega16 Microcontroller is was realized and receiver property was analyzed. PLC exclusive chip APLC-485MA, Microcontroller ATmega16, 5pi bulb type LED and high flux LED, visible light receiving sensor LLS08-A1 were used for transmitter and receiver. Performance was analyzed by designed program and an oscilloscope. It was showed average 20% improved receiver rate rather than bulb type LED in the case of high flux LED through voltage change rate on communication distance and LED type of distance between 10 to 50 cm. The blue LED showed the best performance among measured LED types with above 10% of voltage decreasing rate. But As it gradually becomes more distant, the precise date was difficult to obtain due to weak light. To overcome these sort of problems, specific values such as changing conditions and efficiency value relevant to light emitting parts and visible light receiving sensor should be calculated and continuous study and improvements should also be accomplished for the better communications condition.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1530-1537
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• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.851-865
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• 2021

Enterprise networks in the PyeongChang Winter Olympics were hacked in February 2018. According to a domestic security company's analysis report, attackers destroyed approximately 300 hosts with the aim of interfering with the Olympics. Enterprise have no choice but to rely on digital vaccines since it is overwhelming to analyze all programs executed in the host used by ordinary users. However, traditional vaccines cannot protect the host against variant or new malware because they cannot detect intrusions without signatures for malwares. To overcome this limitation of signature-based detection, there has been much research conducted on the behavior analysis of malwares. However, since most of them rely on a sandbox where only analysis target program is running, we cannot detect malwares intruding the host where many normal programs are running. Therefore, this study proposes a method to detect malware variants in the host through logs rather than the sandbox. The proposed method extracts common behaviors from variants group and finds characteristic behaviors optimized for querying. Through experimentation on 1,584,363 logs, generated by executing 6,430 malware samples, we prove that there exist the common behaviors that variants share and we demonstrate that these behaviors can be used to detect variants.

• The Journal of Industrial Distribution & Business
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• v.13 no.7
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• pp.1-6
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• 2022

Purpose: This study verified the influence of teachers' job characteristics on empowerment and job performance, and the mediating effect of empowerment in the relationship between job characteristics factors and job performance. Research design, data, and methodology: As a factor influencing human resources that influence organizational success or failure, job characteristics induce an important psychological state in organizational members, which affects individual motivation and job satisfaction, thereby achieving the goal of securing stable management and job security. In this study, a questionnaire survey of private academy instructors was conducted and reliability and factor analysis, and multiple regression analysis were used. Results: The purpose of this study was to understand the effect of the job characteristics of academy instructors on empowerment and job performance, and to verify whether empowerment plays a mediating role in the relationship between job characteristics and job performance. Conclusions: As a result of verifying Hypothesis 1, the educational environment, expertise, and social support of academy instructors all had a significant positive (+) effect on job performance. As a result of the verification of Hypothesis 2, empowerment greatly mediated the relationship between the educational environment, expertise, and job performance. However, empowerment did not mediate the relationship between social support for academy instructors and job performance.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.688-701
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• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.169-180
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• 2020

In this study, we explored Technology Acceptance Model(TAM) to introduce Blockchain technology in the medical field. It extracted five external variables(Security, Availability, Reliability, Diversity, Economic feasibility) through previous studies. It set the study model for a path to acceptance intention through the information reliance of recognized easiness and recognized usefulness. As results of empirical analysis, H1-1(Security →Perceived Easiness) was rejected. H1-2(Availability→Perceived Easiness), H1-3(Reliabilit→Perceived Easiness), H1-4(Diversity →Perceived Easiness), H1-5(Economic →Perceived Easiness) were adopted. Hypothesis 2 was a relations between Blockchain's characteristics and Perceived usefulness, all the Hypothesis were adopted. Hypothesis 3 and Hypothesis 4 indicated that H3-1(Perceived Easiness →Perceived usefulness) was rejected but H3-2(Perceived Easiness → information reliability), H3-3(Perceived usefulness → information reliability), and H4(information reliability→acceptance intention) were all adopted. It was confirmed that it is important to emphasize the importance of stability to introduce block chain technology to medical centers, but it was necessary to use a design that can increase the easiness from the prospect of users.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1079-1087
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• 2018

Binary scalar multiplication which is the main operation of elliptic curve cryptography is vulnerable to the side-channel analysis. Especially, it is vulnerable to the side-channel analysis which uses power consumption and electromagnetic emission patterns. Thus, various countermeasures have been studied. However, they have focused on eliminating patterns of data dependent branches, statistical characteristic according to intermediate values, or the interrelationships between data. No countermeasure have been taken into account for the secure design of the key bit check phase, although the secret scalar bits are directly loaded during that phase. Therefore, in this paper, we demonstrate that we can extract secret scalar bits with 100% success rate using a single power or a single electromagnetic trace by performing key bit-dependent attack on hardware implementation of binary scalar multiplication algorithm. Experiments are focused on the $Montgomery-L{\acute{o}}pez-Dahab$ ladder algorithm protected by scalar randomization. Our attack does not require sophisticated pre-processing and can defeat existing countermeasures using a single-trace. As a result, we propose a countermeasure and suggest that it should be applied.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.

• KIEAE Journal
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• v.16 no.6
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• pp.39-50
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• 2016

Purpose: The purpose of this study is to be performed with studio-type housings among urban life-type housings to investigate the physical characteristic and crime-related factors of studios from the viewpoint of the basic principles of crime prevention through environmental design (CPTED). Method: Eight CPTED guidelines available in Korea were reviewed to select 20 planing factors for actual condition investigation. Five single households in studio-type housings in Gwanak-gu, Seoul, were chosen according to the subject screening criteria to perform the actual condition investigation. Results: First, a lighting plan around a building for natural surveillance should consider the building location, relation with the front road, and surrounding facilities. In a building of a piloti structure, the parking lot and the building gate should be arranged in a manner that enables natural surveillance. Second, the shape of the corridors in studio-type housings should be considered to plan the installation of a lighting at the door of each household, the installation of a viewer window at the door of each household, and the arrangement of the elevator. Third, to support access control, an access control system having the function of video and voice communication is recommended to be installed at the building gate. Criteria for the type of security windows and the floors on which security windows should be installed, and the regulations about the CCTV installation inside and outside the building should be prepared. Fourth, to enhance territoriality in parking lots, ground patterns, parking lot gate, and signs may be installed. Fifth, in view of effective utilization and maintenance, lighting facilities should be installed to increase the usability of ground parking lots, and relevant installation criteria should be prepared regarding the type, number, and brightness of the lightings.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.2
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• pp.189-196
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• 2020

In general, fragmented files without signatures and file meta-information are difficult to recover. Multimedia files, in particular, are highly fragmented and have high entropy, making it almost impossible to recover with signature-based carving at present. To solve this problem, research on fragmented files is underway, but research on multimedia files is lacking. This paper is a study that classifies the types of fragmented multimedia files without signature and file meta-information. Extracts the characteristic values of each file type through the frequency differences of specific byte values according to the file type, and presents a method of designing the corresponding Gray-Scale table and classifying the file types of a total of four multimedia types, JPG, PNG, H.264 and WAV, using the CNN (Convolutional Natural Networks) model. It is expected that this paper will promote the study of classification of fragmented file types without signature and file meta-information, thereby increasing the possibility of recovery of various files.