• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.941-944
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• 2011

모바일 클라우드 서비스는 사용자가 모바일 단말에 자원을 가지고 있지 않더라도 인터넷을 통해 외부의 다양한 IT 자원을 제공하는 서비스로서 모바일 단말이 가지는 성능적 한계를 극복시킬 수 있다는 장점과 함께 이용자 수가 증가하고 있다. 그러나 클라우드 컴퓨팅 환경에 존재하는 개인 및 기업의 정보 유출과 같은 문제들은 모바일 클라우드 컴퓨팅 환경에도 그대로 상속되기 때문에 이러한 문제에 대응하기 위해서는 모바일 클라우드 컴퓨팅 환경에서 정보유출을 탐지할 수 있는 이상행위 탐지 알고리즘이 마련되어야 한다. 여기서 이상행위란, 모바일 클라우드 자원에 접근하는 방법에 있어 기존에 인지하고 있던 정상적인 행위에서 벗어나는 행위를 의미하며 이상행위로 판단되는 상황이 발생되는 경우, 이를 정보유출이 발생할 수 있는 상황으로 인지함으로써 적절한 대응을 할 수 있게 된다. 따라서 본 논문에서는 모바일 클라우드 자원의 정보유출을 방지하기 위한 목적으로 자원 접근에 대한 이상행위 탐지 알고리즘 개발 모델을 제시한다. 이상행위 탐지 알고리즘을 개발하고 이를 검증하기 위해서는 이상행위를 일으키는 공격 모델 및 대응 모델이 개발되어야 한다. 따라서 본 논문에서는 인증 및 권한관리의 취약점을 이용하여 위협을 일으키는 공격 모델을 개발하는 방법을 제시하고, 사용자의 접속환경 및 클라우드 자원의 정보 흐름을 분석함으로써 이상행위를 탐지하는 알고리즘을 제시한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.39-42
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• 2023

최근 온라인 게임 산업이 급속도로 확장됨과 더불어 Gamebot과 같은 비정상적인 프로그램으로 인한 게임 서비스 피해사례가 급격하게 증가하고 있다. 특히, 대표적인 게임 장르 중 하나인 FPS(First-Person Shooter)에서 Aimbot의 사용은 정상적인 이용자들에게 재미 요소를 잃어버리게 하고 상대적 박탈감을 일으켜 게임의 수명을 줄이는 원인이 된다. 비정상 게임 이용자의 근절을 위해서 메모리 변조 및 불법 변조 프로그램 접근 차단 기법과 불법 프로그램 사용의 패턴 모니터링과 같은 기법들이 제안되었지만, 우회 프로그램 및 새로운 패턴을 이용한 비정상적인 프로그램의 개발에는 취약하다는 단점이 있다. 따라서, 본 논문에서는 정상적인 게임 이용자의 패턴만 학습함으로써 비정상 이용자 검출을 가능하게 하는 딥러닝 기반 단일 클래스 분류 기법을 제안하며, 가장 빈번하게 발생하는 치트(Cheat) 유형인 FPS 게임 내 Aimbot 사용 감지에 초점을 두었다. 제안된 비정상 게임 이용자 감지 시스템은 정상적인 사용자의 마우스 좌표를 데카르트 좌표계(Cartesian coordinates)와 극좌표계(Polar coordinates)의 형태로 패턴을 추출하는 과정과 정상적인 마우스 동작 기록으로 부터 학습된 LSTM 기반 Autoencoder의 복원 에러에 따른 검출 과정으로 구성된다. 실험에서 제안된 모델은 FPS 게임 내 마우스 동작을 기록한 공개 데이터셋인 CSGO 게임 데이터셋으로 부터 학습되었으며, 학습된 모델의 테스트 결과는 데카르트 좌표계로부터 훈련된 제안 모델이 비정상 게임 이용자를 분류하는데 적합함을 입증하였다.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.43-48
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• 2023

Globally, there is a collaborative effort to achieve global carbon neutrality in response to climate change. In the case of South Korea, greenhouse gas emissions are rapidly increasing, presenting an urgent situation that requires resolution. In this context, this study developed a thermal energy collection device named a 'steam trap' and created an AI model capable of predicting future electricity usage by collecting energy usage data through steam traps. The average accuracy of electricity usage prediction with this AI model was 96.7%, demonstrating high precision. Consequently, the AI model enables the prediction and management of days with high electricity consumption and identifies which facilities contribute to elevated power usage. Future research aims to optimize energy consumption efficiency through efficient equipment operation using anomaly detection in steam traps and standardizing energy management systems, with the ultimate goal of reducing greenhouse gas emissions.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.177-188
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• 2024

As technology evolves, Internet usage continues to grow, resulting in a geometric increase in network traffic and communication volumes. The network path selection process, which is one of the core elements of the Internet, is becoming more complex and advanced as a result, and it is important to effectively manage and analyze it, and there is a need for a representation and visualization method that can be intuitively understood. To this end, this study designs a framework that analyzes network data using BGP, a network path selection method, and applies it to the cyber common operating picture for situational awareness. After that, we analyze the visualization elements required to visualize the information and conduct an experiment to implement a simple visualization. Based on the data collected and preprocessed in the experiment, the visualization screens implemented help commanders or security personnel to effectively understand the network situation and take command and control.

• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.137-144
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• 2005

In this study, we offered the results of geophysical and geochemical survey on the municipal waste disposal area to delineate the size and extent of leachate contamination. Preliminary to intensive geochemical investigation, we performed two geophysical methods to characterize the survey area. Electromagnetic (EM) and magnetic method were used far site investigation. From the EM method, we can get the information of soil conductivity directly related to the leachate of the contaminations and from magnetic anomalies we can find the boundary of landfill which is not identified on the surface due to soil capping. The results of geophysical survey were well matched to those of geochemical method carried out inside and near the landfill. Electric conductivity (EC) of the groundwater sampled from low resistivity anomaly region of EM result was higher than background value and the border estimated from the magnetic survey showed good agreement with that estimated from the soil gas detection survey.

• Korean Journal of Remote Sensing
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• v.14 no.4
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• pp.325-342
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• 1998

Two RADARSAT SAR images with different modes acquired by Canadian Space Agency to test the effectiveness of geological lineament extraction and spring water detection over the Cheju Island. Geological lineaments are poorly developed this basalt dominant volcanic island, but more linear features can be extracted when SAR and TM images are simultaneously analyzed than when TM image alone is used. This results mainly owe to the facts that RADARSAT SAR systems are able to provide data with different frequencies, azimuth, and incidence angles. Distribution of spring water along coast is poorly correlated with geological lineaments or drainage pattern, but those in middle range of mountain region are developed along geological lineaments. Detection of spring water using remotely sensed images are turned out to be very difficult to achieve. Radial shaped sea surface temperature anomaly derived from TM thermal band should be the best candidate for spring water, but the resolution is not high enough. We also investigate the normalized radar cross section (or sigma naught) converted from RADARSAT and ERS-1 SAR data but to discriminate the spring water effectively except where relatively large water mass is observed on land side. Speckle noise and irregularity in physical sea surface condition are the serious obstacles for this application. ERS-1 SAR image acquired in low incidence angle was more useful for geological lineament estimation and water body study than RADARSAT SAR images with high incidence angles. Therefore the selection of incidence angle is critical in geological and spring water applications of SAR images, and low incidence angles less than about 30$^{\circ}$ are recommended to monitor the Cheju volcanic island.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.57-65
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• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.39-48
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• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.