• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.23-33
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• 2004

In this paper, we propose a novel traffic measurement based detection of network attack symptoms on high speed Internet backbone links. In order to do so, we characterize the traffic patterns from the normal and the network attacks appeared on Internet backbone links, and we derive two efficient measures for representing the network attack symptoms at aggregate traffic level. The two measures are the power spectrum and the ratio of packet counts to traffic volume of the aggregate traffic. And, we propose a new methodology to detect networks attack symptoms by measuring those traffic measures. Experimental results show that the proposed scheme can detect the network attack symptoms very exactly and quickly. Unlike existing methods based on Individual packets or flows, since the proposed method is operated on the aggregate traffic level. the computational complexity can be significantly reduced and applicable to high speed Internet backbone links.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.1-8
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• 2021

In this paper, we propose the construction of an IOT that monitors foresters' physical abnormalities in real time, performs emergency measures, and provides alarms for natural disasters or heatstroke such as a nearby forest fire or landslide. Nodes provided to foresters include 6-axis sensors, temperature sensors, GPS, and LoRa, and transmit the measured data to the network server through the gateway using LoRa communication. The network server uses 6-axis sensor data to determine whether or not a forester has any signs of abnormal body, and performs emergency measures by tracking GPS location. After analyzing the temperature data, it provides an alarm when there is a possibility of heat stroke or when a forest fire or landslide occurs in the vicinity. In this paper, it was confirmed that the real-time detection of physical abnormalities of foresters and the establishment of disaster early warning IOT is possible by analyzing the data obtained by constructing a node and a gateway and constructing a network server.

• 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 ${\rho}$.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.561-567
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• 2018

In this paper, we apply this method to the entire process of smart disaster safety management based on the $4^{th}$ industrial revolution to minimize human, social, economic and environment damage from accidents and disasters, prevention evaluation and disaster information collection analysis and real-time detection of field situation. Prevention of $5^{th}$ generation communication system by analysis, contrast by education and training using virtual reality and augmented reality disaster safety management decision support system intelligent robot for recovery, disaster, discovery, reconnaissance relief, and scale analysis of damages were proposed.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1141-1148
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• 2016

This paper proposed a heart attack predictive monitoring system using QRS pattern of ECG for wearable healthcare. It detects abnormal heart pattern with a ECG (X, Y) coordinate pattern DB on wearable monitoring smart watch. We showed the acute heart failure prevention system and method with a proposed scheme. Especially, It proved the method which can do first aid in gold time through abnormal heart analysis with a digital ECG(X, Y) pattern information when acute heart failure occurs.

• 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.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2011.11a
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• pp.514-517
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• 2011

화재감지 및 경보시스템의 설계 목표는 화재발생 초기 단계에서 화재징후를 발견, 피난의 개시를 신속하게 통수하는 것이다. 화재감지기는 다양한 건물 및 환경에 설치되기 때문에 주위 온도 및 가연물의 종류 등 환경적 측면을 심층적으로 고려하지 않으면 적절하게 감지하지 못하는 경우가 발생할 수 있다. 즉, 가연물 종류 등을 고려한 설계에 따라 설치된 화재감지기는 화재시 설계치 대로 조기에 화재를 감지하여 화재 예방 및 화재로 인한 피해를 최소화할 수 있는 것이다. 본 논문은 화재시 조기에 화재를 감지기하여 건축물내 인명피난을 목적으로 설치되는 화재감지기의 응답특성을 분석한 연구이다. 화재감지기의 응답특성을 분석하기 위하여 다양한 가연물을 발생시키고 화재감지기 종류별로 설치한 다음 각 화재감지기의 응답특성을 분석하였다. 그 결과 정온식 열감지기는 열방출률이 적은 화재를 조기에 감지하는 것에 적합하지 않은 것으로 나타났다. 광전식 연기감지기는 회색 계통의 목재류 화재성상에서 응답특성이 떨어졌고, 동일한 공간에서 화원의 수평거리와 동작시간이 비례한다고 볼 수 없었다.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.35-42
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• 2017

In this paper, we propose to implement an integrated monitoring system using log data to reduce the load of analysis task of information security officer and to detect information leak in advance. To do this, we developed a transmission module between different model DBMS that transmits large amount of log data generated by the individual security system (MSSQL) to the integrated monitoring system (ORACLE), and the transmitted log data is digitized by individual and individual and researches about the continuous inspection and measures against malicious users when the information leakage symptom is detected by using the numerical data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2B
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• pp.90-99
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• 2007

It might be more efficient that detections of distributed denial of service (DDoS) attacks are done in backbone domain than in individual local networks or links. However, because existing schemes for detecting DDoS attack symptoms have been focused on individual packets or flows, they require much higher computational complexities. In this paper, we propose an efficient method to detect DDoS attack symptoms in backbone networks. Unlike conventional schemes focused on individual packets or flows, the proposed method is carried at aggregate traffic level. So, our proposed schemes can be operated with very lower computational complexity, and can be run in very high-speed backbone networks.

• Journal of the Korean Geotechnical Society
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• v.26 no.10
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• pp.69-79
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• 2010

To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.