• Journal of Korea Water Resources Association
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• v.39 no.1 s.162
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• pp.35-46
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• 2006

In this study, we proposed a layout of the integrated decision support system in order to prevent the contamination and to manage risk in water supply networks for safe and smooth water supply. We evaluated the priority of risk factors to detect anomaly in water supply networks using PROMETHEE and ANP techniques, which are applied to various Multi-Criteria Decision Making area in Europe and America. To develop the model, we selected pH, residual chlorine concentration, discharge, hydraulic pressure, electrical conductivity, turbidity, block leakage and water temperature as the key data item. We also chose pipe corrosion, pipe burst and water pollution in pipe as the criteria and then we present the results of PROMETHEE and ANP analysis. The evaluation results of the priority of risk factors in water supply networks will provide basic data to establish a contingency plan for accidents so that we can establish the specific emergency response procedures.

• Journal of Navigation and Port Research
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• v.39 no.6
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• pp.479-484
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• 2015

Network RTK is a precise positioning technique using carrier phase correction data from reference stations within the network, and is constantly being researched for improved performance. However, the study for the system accuracy has been performed but system integrity research has not been done as much as system accuracy, because network RTK has been mainly used on surveying for static or kinematic positioning. In this paper, adequate monitoring system for network RTK is designed as basis research for integrity monitoring on network RTK. To this, fault tree on network RTK is analyzed, and a countermeasure is prepared to detect and identify the each fault items. Based these algorithms, monitoring system to use on central processing facility is designed for network RTK service.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.877-889
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• 2016

In this paper, we introduce the basic components of the Cognitive Radio Networks along with possible threats. Specifically, we investigate the SSDF (Spectrum Sensing Data Falsification) attack which is one of the easiest attack to carry out. Despite its simplicity, the SSDF attack needs careful attention in order to build a secure system that resists to it. The proposed scheme utilizes the Anomaly Detection technique to identify malicious users as well as their sensing reports. The simulation results shows that the proposed scheme can effectively detect erroneous sensing reports and thus result in correct detection of the active primary users.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.1110-1119
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• 2006

If water flows through a narrow passage into a medium that keeps the equilibrium of temperature, it causes small temperature difference and makes a temperature anomaly. The seepage or leakage often observed at old dams is a representative example of bringing about a temperature anomaly. Therefore, temperature measurements have been regarded as one of excellent methods that can detect the situation of seepage or leakage. However, because existing temperature measurement methods are based on a single sensor, the application of the method to the whole structure is nearly not possible in technical and economical phases. This paper introduces a temperature monitoring system using a thermal sensor cable that is comprised of addressable thermal sensors connected in parallel at many positions within a single cable. Through various laboratory and field experiments, it has been proved that the temperature monitoring technique can give an useful information about permeability of a medium or connectivity of fractures which have been regarded as difficult problems.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.141-156
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• 2010

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour, and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1547-1556
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• 2022

As mechanical facilities are interacting with each other, the failure of some equipment can affect the entire system, so it is necessary to quickly detect and diagnose the abnormality of mechanical equipment. This study proposes a deep learning model that can effectively diagnose abnormalities in rotating machinery and equipment. CNN is widely used for feature extraction and LSTMs are known to be effective in learning sequential information. In LSTM, the number of parameters and learning time increase as the length of input data increases. In this study, we propose a method of segmenting an input segment signal into shorter-length sub-segment signals, sequentially inputting them to CNN through a time-distributed method for extracting features, and inputting them into LSTM. A failure diagnosis test was performed using the vibration data collected from the motor for ventilation equipment installed at the urban railway station. The experiment showed an accuracy of 99.784% in fault diagnosis. It shows that the proposed method is effective in the fault diagnosis of rotating machinery and equipment.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.864-872
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• 2015

The purpose of this study is to design the algorithm, Predictive Service Component - PSC, for forecasting and judging obsolescence of solar system that is implemented based on the micro-inverter. PSC proposed in this study is suitable for monitoring of distributed power generation systems. It provides a diagnosis functionality to detect failures and anomaly events. It also can determine the aging of PV systems. The conclusion of this study shows the research and development of this kind of integrated system using PSC will be needed more and varied in the near future.

• Convergence Security Journal
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• v.24 no.3
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• pp.67-79
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• 2024

Attackers targeting critical infrastructure, such as energy plants, conduct intelligent and sophisticated attacks that conceal their traces until their objectives are achieved. Manipulating measurement data of cyber-physical systems, which are connected to the physical environment, directly impacts human safety. Given the unique characteristics of cyber-physical systems, a differentiated approach is necessary, distinct from traditional IT environment anomaly detection and identification methods. This study proposes a methodology that integrates both recursive filtering and an entropy-based approach to identify maliciously manipulated measurement data, considering the characteristics of cyber-physical systems. By applying the proposed approach to synthesized data based on a publicly available industrial control system security dataset in our research environment, the results demonstrate its effectiveness in identifying manipulated operational data.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.127-134
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• 2021

In this study, an anomaly detection (AD) algorithm was implemented to detect the failure of a marine air compressor. A lab-scale experiment was designed to produce fault datasets (time-series electric current measurements) for 10 failure modes of the air compressor. The results demonstrated that the temporal pattern of the datasets showed periodicity with a different period, depending on the failure mode. An AD model with a convolutional autoencoder was developed and trained based on a normal operation dataset. The reconstruction error was used as the threshold for AD. The reconstruction error was noted to be dependent on the AD model and hyperparameter tuning. The AD model was applied to the synthetic dataset, which comprised both normal and abnormal conditions of the air compressor for validation. The AD model exhibited good detection performance on anomalies showing periodicity but poor performance on anomalies resulting from subtle load changes in the motor.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.