• Electronics and Telecommunications Trends
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• v.20 no.1 s.91
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• pp.22-32
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• 2005

기존의 사이버 공격은 특정 호스트나 서버를 목표로 하여 정보의 탈취 및 변경 등에 집중되었으나, 현재는 직접 혹은 간접적으로 과다 트래픽을 유발하여 네트워크 서비스를 마비시키는 방향으로 그 경향이변하고 있다. 이런 사이버 공격을 방지하여 네트워크의 안정적인 서비스의 제공을 위해서는 공격 징후나 이상 징후를 탐지하고 네트워크 차원에서 이에 대한 대응 방안을 결정하여 이를 네트워크 상에 강제할 수 있는 체계적인 보안 관리가 이루어져야 한다. 또한 네트워크 각 운용 주체별로 개별 보안 상황에 대해 적용할 보안 정책이 다르므로 이를 모델링하고 적용할 수 있는 방법이 제공되어야 한다. 본 논문에서는 정책 기반 네트워크 보안 관리 기능을 수행하기 위해 필요한 공격 및 이상 징후의 탐지, 그에 대한 대응과 이런 일련의 작업에 보안 정책을 강제하기 위한 보안 정책관련 연구 동향에 대해 다루도록한다.

• Information and Communications Magazine
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• v.20 no.8
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• pp.63-70
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• 2003

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.412-417
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• 2019

This study was proposed to investigate vital signs in subjects undergoing high magnetic field (3T) MR imaging for provide basic data on causes of claustrophobia as few previous studies were conducted on this special issue. Vital signs of 104 patients were monitored before and during the clinically indicated MR examinations to identify any relationship between MR scanning and the vital signs. An increase of systolic, diastolic blood pressure and pulse pressure were observed. However, they were not statistically significant(p>0.05), which meant the vital signs measured before and during the MRI scanning showed no significant change. This study is considered to be meaningful basic data for analyzing the links between vital sign fluctuations on claustrophobia during routine clinical MR examinations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2037-2042
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• 2017

The manufacturing facility is generally operated by a pre-set program under the existing factory automation system. On the other hand, the manufacturing facility must decide how to operate autonomously in Industry 4.0. Determining the operation mode of the production facility itself means, for example, that it detects the abnormality such as the deterioration of the facility at the shop-floor, prediction of the occurrence of the problem, detection of the defect of the product, In this paper, we propose a manufacturing process modeling using a queue for detection of manufacturing process abnormalities at the shop-floor, and detect abnormalities in the modeling using SVM, one of the machine learning techniques. The queue was used for M / D / 1 and the conveyor belt manufacturing system was modeled based on ${\mu}$, ${\lambda}$, and ${\rho}$. SVM was used to detect anomalous signs through changes in ${\rho}$.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.1-13
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• 2022

With the development of network technologies, the security to protect organizational resources from internal and external intrusions and threats becomes more important. Therefore in recent years, the anomaly detection algorithm that detects and prevents security threats with respect to various security log events has been actively studied. Security anomaly detection algorithms that have been developed based on rule-based or statistical learning in the past are gradually evolving into modeling based on machine learning and deep learning. In this study, we propose a deep-autoencoder model that transforms LSTM-autoencoder as an optimal algorithm to detect insider threats in advance using various machine learning analysis methodologies. This study has academic significance in that it improved the possibility of adaptive security through the development of an anomaly detection algorithm based on unsupervised learning, and reduced the false positive rate compared to the existing algorithm through supervised true positive labeling.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.135-143
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• 2022

As the world has implemented social distancing and telecommuting due to the spread of COVID-19, real-time streaming sessions based on routing protocols have increased dependence on the Internet due to the activation of video and voice-related content services and cloud computing. BGP is the most widely used routing protocol, and although many studies continue to improve security, there is a lack of visual analysis to determine the real-time nature of analysis and the mis-detection of algorithms. In this paper, we analyze BGP data, which are powdered as normal and abnormal, on a real-world basis, using an anomaly detection algorithm that combines statistical and post-processing statistical techniques with Rule-based techniques. In addition, we present an interactive spatio-temporal analysis plan as an intuitive visualization plan and analysis result of the algorithm with a map and Sankey Chart-based visualization technique.

• The Journal of Bigdata
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• v.7 no.1
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• pp.113-124
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• 2022

Various methods using machine learning and big data have been applied to prevent failures in Data Centers. However, there are many limitations to referencing individual equipment-based performance indicators or to being practically utilized as an approach that does not consider the infrastructure operating environment. In this study, the performance indicators of individual infrastructure equipment are integrated monitoring and the performance indicators of various equipment are segmented and graded to make a single numerical value. Data pre-processing based on experience in infrastructure operation. And an ensemble of RRCF (Robust Random Cut Forest) analysis and Prophet analysis model led to reliable analysis results in detecting anomalies. A failure analysis system was implemented to facilitate the use of Data Center operators. It can provide a preemptive response to Data Center failures and an appropriate tuning time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.281-288
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• 2024

The sixth generation(6G) wireless communication technology is advancing toward ultra-high speed, ultra-high bandwidth, and hyper-connectivity. With the development of communication technologies, the formation of a hyper-connected society is rapidly accelerating, expanding from the IoT(Internet of Things) to the IoE(Internet of Everything). However, at the same time, security threats targeting IoT devices have become widespread, and there are concerns about security incidents such as unauthorized access and information leakage. As a result, the need for security-enhancing solutions is increasing. In this paper, we implement an autoencoder-based anomaly detection model utilizing real-time collected network traffics in respond to IoT security threats. Considering the difficulty of capturing IoT device traffic data for each attack in real IoT environments, we use an unsupervised learning-based autoencoder and implement 6 different autoencoder models based on the use of noise in the training data and the dimensions of the latent space. By comparing the model performance through experiments, we provide a performance evaluation of the anomaly detection model for detecting abnormal network traffic.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.119-125
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• 2022

With the recent increase in dementia patients due to aging, measures to prevent their wandering behavior and disappearance are urgently needed. To solve this problem, various authentication methods and location detection techniques have been introduced, but the security problem of personal authentication and a system that can check indoor and outdoor overall was lacking. In order to solve this problem, various authentication methods and location detection techniques have been introduced, but it was difficult to find a system that can check the security problem of personal authentication and indoor/outdoor overall. In this study, we intend to propose a system that can identify personal authentication, basic health status, and overall location indoors and outdoors by using wristband-type wearable devices in a private blockchain environment. In this system, personal authentication uses ECG, which is difficult to forge and highly personally identifiable, Bluetooth beacon that is easy to use with low power, non-contact and automatic transmission and reception indoors, and DGPS that corrects the pseudorange error of GPS satellites outdoors. It is intended to detect wandering behavior and abnormal signs by locating the patient. Through this, it is intended to contribute to the prompt response and prevention of disappearance in case of wandering behavior and abnormal symptoms of dementia patients living at home or in nursing homes.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.315-320
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• 1996

영광 3호기의 정지냉각펌프 성능감시 설비로는 펌프 유량계, 입구압, 출구압, 모터전류 등이 있으며 현장에서 펌프의 소음 감시나 진동 측정 등을 통하여 펌프 건전성을 확인할 수 있다. 부분충수운전중 여러 연구결과 제시된 펌프의 이상징후 증상은 펌프의 소음 증가, 유량계 또는 모터전류의 불규칙 요동이 있으나 정량적인 값을 제시하지 못하고 있으며 공기유입량에 대한 운전제한 근거만 정량적으로 제시되고 있다. 즉, WCAP-l1916에 따른 펌프의 손상 판단 근거는 연속적인 공기 흡입의 경우 2%이내, 간헐적인 공기흡입의 경우 5%를 제시하고 있다. 영광 3 호기의 부분충수운전시 펌프 입구압력을 제외한 다른 펌프 성능감시 변수들은 허용오차 이내로 별다른 펌프 이상 징후를 발견하지 못하였다. 그러나 펌프 입구압력 기록계의 입구압력 및 진동폭 변화는 정지냉각유량률, RCS 수위, 증기발생기 노즐댐 설치 유무에 따라 민감한 변화를 보여주었으며, 펌프의 건전성 감시에 가장 효과적인 변수임을 보여주었다.