• Atmosphere
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• v.29 no.1
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• pp.75-86
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• 2019

In this paper, we introduce a novel Climate & Environment Database System (CEDS). The CEDS is developed by the National Institute of Meteorological Sciences (NIMS) to provide easy and efficient user interfaces and storage management of climate model data, so improves work efficiency. In uploading the data/files, the CEDS provides an option to automatically operate the international standard data conversion (CMORization) and the quality assurance (QA) processes for submission of CMIP6 variable data. This option increases the system performance, removes the user mistakes, and increases the level of reliability as it eliminates user operation for the CMORization and QA processes. The uploaded raw files are saved in a NAS storage and the Cassandra database stores the metadata that will be used for efficient data access and storage management. The Metadata is automatically generated when uploading a file, or by the user inputs. With the Metadata, the CEDS supports effective storage management by categorizing data/files. This effective storage management allows easy and fast data access with a higher level of data reliability when requesting with the simple search words by a novice. Moreover, the CEDS supports parallel and distributed computing for increasing overall system performance and balancing the load. This supports the high level of availability as multiple users can use it at the same time with fast system-response. Additionally, it deduplicates redundant data and reduces storage space.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.613-621
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• 2019

Recently, with the development of the Internet of Things (IoT) and cloud computing technologies, security threats have increased as malicious codes infect IoT devices, and new malware spreads ransomware to cloud servers. In this study, we propose a threat-detection technique that checks obfuscated script patterns to compensate for the shortcomings of conventional signature-based and behavior-based detection methods. Proposed is a malicious code-detection technique that is based on malicious script-pattern analysis that can detect zero-day attacks while maintaining the existing detection rate by registering and checking derived distribution patterns after analyzing the types of malicious scripts distributed through websites. To verify the performance of the proposed technique, a prototype system was developed to collect a total of 390 malicious websites and experiment with 10 major malicious script-distribution patterns derived from analysis. The technique showed an average detection rate of about 86% of all items, while maintaining the existing detection speed based on the detection rule and also detecting zero-day attacks.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.121-132
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• 2019

As the 4th industrial age enters, the number of IoT devices is exploding. Accordingly, the importance of security is also increasing in proportion to the increasing number of security incidents of IoT devices. However, due to the limited performance of IoT devices, there are limitations to applying existing security solutions. Therefore, a new automatic firmware distribution solution is needed to solve this problem. To solve this problem, we propose a new automatic firmware update system that uses a hybrid blockchain that combines a public blockchain and a private blockchain. The public blockchain allows various firmware providers to distribute firmware using a common system. Private blockchain solves the transaction overload problem of the public blockchain and facilitates the management of IoT devices. It also uses distributed file storage to ensure high availability without failing. Therefore, this system is expected to be very effective for improving the security of IoT devices.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.57-65
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• 2019

Creative works such as webtoon and web novel are part of property rights. However, illegal copies of them are distributed on the internet easily, which raises social issues in today's society. In order to tackle these problems, this paper proposes and presents a blockchain based copyright management system that ensures forgery prevention, robust security features, improving trading performance, cost-effective, and enhanced visibility. The system allows a user to register creative works formally just the same as before registration and also to register simple creative ideas just anytime. In the latter case, if an idea or a thought flashes across through somebody's mind, he or she can register it to the system immediately without formal registration process and afterward, can utilize a way to prove its originality through the system. Regarding large size images and video files of creative works, the system reduces data size and storage volume sharply to be processed by network entities by storing original creative works separately and including only the hash result of creative works to the transactions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.4
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• pp.111-122
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• 2008

Web service is defined to support interoperable machine to machine interaction over a network and defined as distributed technologies. Recently in web service environment, security has become one of the most critical issues. An attacker may expose user privacy and service information without authentication. Furthermore, the users of web services must temporarily delegate some or all of their behalf. This results in the exposure of user privacy information by agents. We propose a delegation model for providing safety of web service and user privacy in ubiquitous computing environments. In order to provide safety of web service and user privacy, XML-based encryption and a digital signature mechanism need to be efficiently integrated. In this paper, we propose web service management server based on XACML, in order to manage services and policies of web service providers. For this purpose, we extend SAML to declare delegation assertions transferred to web service providers by delegation among agents.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.54-65
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• 2021

The movement control order and shutting down educational institution in Saudi Arabia has jeopardized the teaching and learning process. Education was shifted to distance learning in order to avoid any academic loss. In the middle of the Covid-19 crisis, there is a need to assess the full image of e-learning in Saudi Arabia. To investigate student and teachers' perception and acceptance, parents' attitudes and believes about distance education are the main goals of the study. The mix-method research design was employed to collect data. Three surveys were distributed to 100 students and 50 teachers and 50 parents from different educational institutions in Saudi Arabia, while semi-structured interviews were conducted with 10 parents. Random stratified and convenient sampling methods were adopted. Both descriptive and content analysis was conducted using SPSS25.0 and NVIVO software for quantitative and qualitative data accordingly. The findings showed that students are comfortable with remote education and are receiving enough support from schools and instructors but they think online education can't replace conventional face-to-face learning. Moreover, the results showed that teachers are having challenges in preparing online classes because of the development of conducting online classes and the lack of training. However, parents showed negative attitudes regarding the benefits and values of remote education and preferred conventional learning styles in elementary schools. Parents tended to reject and resist distance learning for several reasons: professional knowledge and lack of time to support their young kids in online classes, the shortcomings of e-learning, young children's inadequate self-regulation. Saudi parents are neither trained nor ready to use e-learning. The study provided suggestion and implications for teacher education and policymakers.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.97-110
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• 2022

Federated learning is a learning method capable of performing model learning without transmitting learning data. The IoT or healthcare field is sensitive to information leakage as it deals with users' personal information, so a lot of attention should be paid to system design, but when using federated-learning, data does not move from devices where data is collected. Accordingly, many federated-learning implementations have been developed, but detailed research on system design for the development and operation of systems using federated learning is insufficient. This study shows that measures for the life cycle, code version management, model serving, and device monitoring of federated learning are needed to be applied to actual projects and distributed to IoT devices, and we propose a design for a development environment that complements these points. The system proposed in this paper considered uninterrupted model-serving and includes source code and model version management, device state monitoring, and server-client learning schedule management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.13-18
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• 2022

In this paper, we proposed VW-FedAVG(Validation based Weighted FedAVG) which updates the global model by weighting according to performance verification from the models of each device participating in the training. The first method is designed to validate each local client model through validation dataset before updating the global model with a server side validation structure. The second is a client-side validation structure, which is designed in such a way that the validation data set is evenly distributed to each client and the global model is after validation. MNIST, CIFAR-10 is used, and the IID, Non-IID distribution for image classification obtained higher accuracy than previous studies.