• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.15-22
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• 2020

The 4th industrial revolution and the rapid change in the data environment revealed technical limitations in the existing relational database(RDB). As a new analysis method for unstructured data in all fields such as IDC/finance/insurance, interest in graph database(GDB) technology is increasing. The graph database is an efficient technique for expressing interlocked data and analyzing associations in a wide range of networks. This study extended the existing RDB to the GDB model and applied machine learning algorithms (pattern recognition, clustering, path distance, core extraction) to detect new abnormal signs. As a result of the performance analysis, it was confirmed that the performance of abnormal behavior(about 180 times or more) was greatly improved, and that it was possible to extract an abnormal symptom pattern after 5 steps that could not be analyzed by RDB.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1019-1022
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• 2005

본 논문에서는 현재 활발한 연구개발이 이루어지고 있는 유비쿼터스 컴퓨팅, 센서 네트워크 기술을 적용한 무인 감시/추적 시스템을 제시한다. 본 논문의 무인 감시/추적 시스템은 센서네트워크 기술, 다중센서 융합에 의한 탐지 및 위치 인식기술, 무인 감시/추적 알고리즘으로 구성되어 있다. 센서네트워크는 센싱 데이터를 실시간으로 전송하기 위해 노드의 주소를 기반으로 하는 계층적 멀티홉 라우팅 기법을 제안하였다. 침입자와 추적자의 위치 인식은 자기센서 및 초음파센서를 가진 센서모듈들로부터 얻어진 센싱 정보를 융합하고, 이를 확률적으로 침입자 및 추적자의 위치를 결정하는 Particle Filter를 적용한 위치인식 알고리즘을 통해 이루어진다. 추적 알고리즘은 무인 자율 추적을 위해 이동벡터에 기반한 알고리즘이다.

• Journal of KIISE:Information Networking
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• v.33 no.2
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• pp.131-138
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• 2006

To achieve security in wireless sensor networks(WSN), it is important to be able to encrypt and authenticate messages sent among sensor nodes. Due to resource constraints, many key agreement schemes used in general networks such as Diffie-Hellman and public-key based schemes are not suitable for wireless sensor networks. The current pre-distribution of secret keys uses q-composite random key and it randomly allocates keys. But there exists high probability not to be public-key among sensor nodes and it is not efficient to find public-key because of the problem for time and energy consumption. To remove problems in pre-distribution of secret keys, we propose a new cryptographic key management protocol, which is based on the clustering scheme but does not depend on probabilistic key. The protocol can increase efficiency to manage keys because, before distributing keys in bootstrap, using public-key shared among nodes can remove processes to send or to receive key among sensors. Also, to find outcompromised nodes safely on network, it selves safety problem by applying a function of lightweight attack-detection mechanism.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.703-711
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• 2018

Water distribution system (WDS) pipe bursts are caused from excessive pressure, pipe aging, and ground shift from temperature change and earthquake. Prompt detection of and response to the failure event help prevent large-scale service interruption and catastrophic sinkhole generation. To that end, this study proposes a improved Western Electric Company (WECO) method to improve the detection effectiveness and efficiency of the original WECO method. The original WECO method is an univariate Statistical Process Control (SPC) technique used for identifying any non-random patterns in system output data. The improved WECO method multiples a threshold modifier (w) to each threshold of WECO sub-rules in order to control the sensitivity of anomaly detection in a water distribution network of interest. The Austin network was used to demonstrated the proposed method in which normal random and abnormal pipe flow data were generated. The best w value was identified from a sensitivity analysis, and the impact of measurement frequency (dt = 5, 10, 15 min etc.) was also investigated. The proposed method was compared to the original WECO method with respect to detection probability, false alarm rate, and averaged detection time. Finally, this study provides a set of guidelines on the use of the WECO method for real-life WDS pipe burst detection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1942-1950
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• 2013

This paper suggests a new method of detecting attacks of network traffic by visualizing original traffic data and applying multi-class SVM (support vector machine). The proposed method first generates 2D images from IP and ports of transmitters and receivers, and extracts linear patterns and high intensity values from the images, representing traffic attacks. It then obtains variance of ports of transmitters and receivers and extracts the number of clusters and entropy features using ISODATA algorithm. Finally, it determines through multi-class SVM if the traffic data contain DDoS, DoS, Internet worm, or port scans. Experimental results show that the suggested multi-class SVM-based algorithm can more effectively detect network traffic attacks.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.1-9
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• 2019

We intend to collect and analyze traffic efficiently in order to detect copyright infringement that illegally share contents on Tor network. We have designed and implemented a Tor traffic collection system using multiple virtual machines. We use a number of virtual machines and Mini PCs as clients to connect to Tor network, and automate both the collection and refinement processes in the traffic collection server through script-based test client software. Through this system, only the necessary field data on Tor network can be stored in the database, and only 95% or more of recognition of Tor traffic is achieved.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.31-37
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• 2021

In this paper, we propose an outlier detection algorithm called C-SCGP to prevent the degradation of localization performance based on RSS (Received Signal Strength) and AOA (Angle of Arrival) in the presence of outliers in wireless sensor networks. Since the accuracy of target estimation can significantly deteriorate due to various cause of outliers such as malfunction of sensor, jamming, and severe noise, it is important to detect and filter out all outliers. The single cluster graph partitioning (SCGP) algorithm has been widely used to remove such outliers. The proposed continuous-SCGP (C-SCGP) algorithm overcomes the weakness of the SCGP that requires the threshold and computing probability of outliers, which are impratical in many applications. The results of numerical simulations show that the performance of C-SCGP without setting threshold and probability computation is the same performance of SCGP.

• Journal of Korea Multimedia Society
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• v.15 no.7
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• pp.910-919
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• 2012

We have observed huge success in social network services like Facebook and Twitter, and many researchers have done their analysis on these services. As massive data observed by users is produced on Twitter, many researchers have been conducting research to detect an event on Twitter. Some of them developed a system to detect the earthquakes or to find the local festivals. However, they did not consider the credibility of location information on Twitter although their systems were using the location information. In this paper, we analyze the credibility of the profile location and the correlation between the spatial attributes on Twitter as the preliminary research of the event detection system on Twitter. We analyzed 0.5 million Twitter users in Korea and 2.8 million users around the world. 49.73% of the users in Korea and 90.64% of the users in the world posted tweets in their profile locations. This paper will be helpful to understand the credibility of the spatial attributes on Twitter when the researchers develop an application using them.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.459-469
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• 2023

The number of IoT devices is explosively increasing due to the development of embedded equipment and computer networks. As a result, cyber threats to IoT are increasing, and currently, malicious codes are being distributed and infected to IoT devices and exploited for DDoS. Currently, IoT devices that are the target of such an attack have various installation environments and have limited resources. In addition, IoT devices have a characteristic that once set up, the owner does not care about management. Because of this, IoT devices are becoming a blind spot for management that is easily infected with malicious codes. Because of these difficulties, the threat of malicious codes always exists in IoT devices, and when they are infected, responses are not properly made. In this paper, we will design an malware detection system for IoT in consideration of the characteristics of the IoT environment and present detection rules suitable for use in the system. Using this system, it will be possible to construct an IoT malware detection system inexpensively and efficiently without changing the structure of IoT devices that are already installed and exposed to cyber threats.