• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.135-140
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• 2017

The demand for aquatic products in the domestic and overseas is increased, so that the aquaculture industry can achieve high performance by controlling and standardizing the production even with a relatively small amount of resources compared with existing fisheries. However, traditional method has problems of low productivity such as natural disasters and ecosystem pollution, and it is necessary to develop a new culture system that can move to the optimal culture site. In order to find the optimal location, you need to collect and analyze the necessary data such as temperature and DO in real time. Data analysis was performed by using K-means clustering method based on machine learning, so that it was possible to decision when and where to move the farm by repeated unsupervised learning. The proposed research could solve the problems of low productivity such as natural disasters and ecosystem pollution if applied to regressive fish farmers.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.359-368
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• 2013

Mashup is a web application that combines several different sources to create new services using Open APIs(Application Program Interfaces). Although the mashup has become very popular over the last few years, there are several challenging issues when combining a large number of APIs into the mashup, especially when composite APIs are manually integrated by mashup developers. This paper proposes a novel algorithm for automatic Open API composition. The proposed algorithm consists of constructing an operation connecting graph and searching composition candidates. We construct an operation connecting graph which is based on the semantic similarity between the inputs and the outputs of Open APIs. We generate directed acyclic graphs (DAGs) that can produce the output satisfying the desired goal. In order to produce the DAGs efficiently, we rapidly filter out APIs that are not useful for the composition. The algorithm is evaluated using a collection of REST and SOAP APIs extracted from ProgrammableWeb.com.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.77-82
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• 2019

AI is applied increasingly to overall industries such as living, medical, financial service, autonomous car, etc. thanks to rapid technology development. AI-leading countries are strengthening their competency to secure competitiveness since AI is positioned as the core technology in $4^{th}$ Industrial Revolution. Although Korea has the competitive IT infra and human resources, it lags behind traditional AI-leaders like United States, Canada, Japan and, even China which devotes all its might to develop intelligent technology-intentive industry. AI is the critical technology influencing on the national industry in the near future according to advancement of intelligent information society so that concentration of capability is required with national interest. Also, joint development with global AI-leading companies as well as development of own technology are crucial to prevent technology subordination. Additionally, regulatory reform and preparation of related law are very urgent.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.93-98
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• 2021

Due to the development of deep learning and AI, the scale of the model has grown, and it has been integrated into other fields to blend into our lives. However, in environments with limited resources such as embedded devices, it is exist difficult to apply the model and problems such as power shortages. To solve this, lightweight methods such as clouding or offloading technologies, reducing the number of parameters in the model, or optimising calculations are proposed. In this paper, quantization of learned models is applied to ONNX models used in various framework interchange formats, neural network structure and inference performance are compared with existing models, and various module methods for quantization are analyzed. Experiments show that the size of weight parameter is compressed and the inference time is more optimized than before compared to the original model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.997-1008
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• 2022

Today, AI(Artificial Intelligence) technology is being extensively researched in various fields, including the field of malware detection. To introduce AI systems into roles that protect important decisions and resources, it must be a reliable AI model. AI model that dependent on training dataset should be verified to be robust against new attacks. Rather than generating new malware detection, attackers find malware detection that succeed in attacking by mass-producing strains of previously detected malware detection. Most of the attacks, such as adversarial attacks, that lead to misclassification of AI models, are made by slightly modifying past attacks. Robust models that can be defended against these variants is needed, and the Robustness level of the model cannot be evaluated with accuracy and recall, which are widely used as AI evaluation indicators. In this paper, we experiment a framework to evaluate robustness level by generating an adversarial sample based on one of the adversarial attacks, C&W attack, and to improve robustness level through adversarial training. Through experiments based on malware dataset in this study, the limitations and possibilities of the proposed method in the field of malware detection were confirmed.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.123-130
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• 2022

Recently, studies on the detection and classification of Android malware based on API Call sequence have been actively carried out. However, API Call sequence based malware classification has serious limitations such as excessive time and resource consumption in terms of malware analysis and learning model construction due to the vast amount of data and high-dimensional characteristic of features. In this study, we analyzed various classification models such as LightGBM, Random Forest, and k-Nearest Neighbors after significantly reducing the dimension of features using PCA(Principal Component Analysis) for CICAndMal2020 dataset containing vast API Call information. The experimental result shows that PCA significantly reduces the dimension of features while maintaining the characteristics of the original data and achieves efficient malware classification performance. Both binary classification and multi-class classification achieve higher levels of accuracy than previous studies, even if the data characteristics were reduced to less than 1% of the total size.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.51-56
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• 2022

With the development of industrialization, the amount of food and waste is rapidly increasing. Accordingly, the government is aware of the seriousness and is making efforts in various ways to reduce it. As a part of that, the volume-based food system was introduced, and although there were several trials and errors at the beginning of the introduction, it shows a reduction effect of 20 to 30%. These results suggest that the volume-based food system is being established. However, the waste is caused by foreign substances in the process of recycling resources by collecting them from the 1st collection to the 2nd collection process. Therefore, in this study, to solve these problems fundamentally, artificial intelligence is applied to classify foreign substances and improve them. Due to the nature of food waste, there is a limit to obtaining many images, so we compare several models based on CNNs and classify them as abnormal data, that is, CNN-based models are trained on various types of foreign substances, and then models with high accuracy are selected. We intend to prepare improvement measures for maintenance, such as manpower input to protect equipment and classify foreign substances by applying it.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.527-535
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• 2023

To develop experts capable of responding to cyber security incidents, numerous institutions have established cyber training facilities to cultivate security professionals equipped with effective defense strategies. However, these challenges such as limited resources, scenario-based content development, and cost constraints. To address these issues, this paper proposes a virtual infrastructure variation generation framework. It provides customized, diverse IT infrastructure environments for each organization, allowing cyber defense trainers to accumulate a wide range of experiences. By leveraging Infrastructure-as-Code (IaC) containers and employing Word2Vec, a natural language processing model, mutable code elements are extracted and trained, enabling the generation of new code and presenting novel container environments.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.3
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• pp.35-64
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• 2024

The purpose of this study is to develop educational strategies and models by applying future-oriented elements of libraries, which are transitioning into integrated spaces through collaboration with archives and museums, into school library education. This study focuses on the changes where libraries expand their spaces and roles in response to the trends of a pluralistic society. The term 'Larchiveum' was established to represent a knowledge and culture complex space. The concepts and functions of this term were identified, and the characteristics of materials collected and managed by archives, museums, and art galleries, as well as domestic and international educational information services, were analyzed to explore ways to integrate them into school library education. Based on the ASSURE instructional design model, which emphasizes the effective selection and use of teaching and learning media, this study developed Larchiveum strategies, including 'Inquiry-based Information Utilization,' 'Curriculum Integration and Convergence,' 'Collaboration, Sharing, and Dissemination,' and 'Digital Archive,' and structured the educational content accordingly. The procedures and steps were refined through the evaluation and feedback of field experts, leading to the development of a collaborative educational model that incorporates Larchiveum strategies, resources, and inquiry-based instruction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.