• Convergence Security Journal
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• v.18 no.5_1
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• pp.93-98
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• 2018

Korea Hydro & Nuclear Power Co., Ltd., Korea Electric Power Corporation, and Korea South-East Power Corporation are major infrastructure facilities of power supplying countries. If a malicious hacking attack occurs, the damage is beyond the imagination. In fact, Korea Hydro & Nuclear Power has been subjected to a hacking attack, causing internal information to leak and causing social big problems. In this paper, we propose a strategy and countermeasures for stabilization of various power generation control systems by analyzing the environment and the current status of power generation control system for convergence security research, which is becoming a hot issue. We propose a method to normalize and integrate data types from various physical security systems (facilities), IT security systems, access control systems, to control the whole system through convergence authentication, and to detect risks through fusion control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• Journal of the Korean Institute of Gas
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• v.14 no.5
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• pp.13-18
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• 2010

This paper presents the development of an integrated control and safety management system for 9% nickel steel LNG storage tank. The new system added the measuring equipment of pressure, displacement and force compared to the conventional measurement and control system. The measured data has simultaneously been processed by integrating and analyzing with new control equipments and safety management systems. The integrated control and safety management system, which may increase a safety and efficiency of a super-large full containment LNG storage tank, added additional pressure gauges and new displacement/force sensors at the outer side wall and a welding zone of a stiffener and top girder of an inner tank, and the inner side wall of a corner protection tank. The displacement and force sensors may provide failure clues of 9% nickel steel structures such as an inner tank and a corner protection, and a LNG leakage from the inner tank. The conventional leak sensor may not provide proper information on 9% nickel steel tank fracture even though LNG is leaked until the leak detector, which is placed at the insulation area between an inner tank and a corner protection tank, sends a warning signal. Thus, the new integrated control and safety management system is to collect and analyze the temperature, pressure, displacement, force, and LNG density, which are related to the tank system safety and leakage control from the inner tank. The digital data are also measured from control systems such as displacement and force of 9% nickel steel tank safety, LNG level and density, cool-down process, leakage, and pressure controls.

• Korean Chemical Engineering Research
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• v.56 no.1
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• pp.133-138
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• 2018

Safety management has become even more important because of the safety and environmental issues that have arisen since the 2000s. However, the safety study requires many empirical data, so there are many limitations. In the case of pipe safety, simulation programs exist, but it is difficult to get data about the pipe internal erosion of the pipe. In this study, the erosion rate of the pipe elbow was simulated using computational fluid dynamics (CFD). Also, the failure period of the pipe was calculated by the limit state function using erosion rate. In the case of CFD pipe, a sample which is actually operated in Yeosu industrial complex was used, and the geometry and mesh formation were rationalized in terms of typical fluid dynamics simulations. Using the Discrete Phase Model (DPM) and the corrosion model, the erosion rate ($3.09227mm{\cdot}yr^{-1}$) was obtained from CFD simulations. As a result of applying the erosion rate to the limit state function, we obtained the pipe failure period value, 14.2 years to trigger a leak and 28.2 years to trigger a burst. Through these processes, we concluded that pipe erosion is one of the major failure modes. In addition to the results, this study has significance for suggesting the methodology of the pipe safety study.

• Journal of the Korean Institute of Gas
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• v.25 no.5
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• pp.1-10
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• 2021

Semiconductor Photo Resist (PR) automation equipment uses a mixture of several flammable substances, and when it leaks during the process, it can lead to various accidents, therefore, risk assessment is necessary. This study analyzed the frequency of leakage of Acetone and PGMEA used in PR automation equipment and the frequency at which such leakage could lead to a fire accident through the frequency analysis method, and evaluated the need for additional risk reduction measures in the current facility. Based on the process leak data and ignition probability data of IOGP, leak frequency analysis and ignition probability were derived, and the frequency of actual fire accidents was analyzed by combining them. The frequency of material leakage in semiconductor PR process is 7.30E-03/year, and fire accidents can occur by acetone that exists above the flash point when the material is leaked, the frequency was calculated at the level of 1.24E-05/year. According to the UK HSE, for a major accident occurring with a frequency of 1.24E-05/year, it is defined as "Broadly Acceptable", a level that does not require additional measures for risk reduction when it causes 7 or less deaths, and due to the process operated by two people, no additional risk reduction are required.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1027-1041
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• 2015

Recently, there are rapidly increasing cases of APT (Advanced Persistent Threat) attacks such as Verizon(2010), Nonghyup(2011), SK Communications(2011), and 3.20 Cyber Terror(2013), which cause leak of confidential information and tremendous damage to valuable assets without being noticed. Several anomaly detection technologies were studied to defend the APT attacks, mostly focusing on detection of obvious anomalies based on known malicious codes' signature. However, they are limited in detecting APT attacks and suffering from high false-negative detection accuracy because APT attacks consistently use zero-day vulnerabilities and have long latent period. Detecting APT attacks requires long-term analysis of data from a diverse set of sources collected over the long time, real-time analysis of the ingested data, and correlation analysis of individual attacks. However, traditional security systems lack sophisticated analytic capabilities, compute power, and agility. In this paper, we propose a Fast Data based real-time abnormal behavior detection system to overcome the traditional systems' real-time processing and analysis limitation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4662-4679
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• 2015

Currently, the leakage of internal information has emerged as one of the most significant security concerns in enterprise computing environments. Especially, damage due to internal information leakage by insiders is more serious than that by outsiders because insiders have considerable knowledge of the system's identification and password (ID&P/W), the security system, and the main location of sensitive data. Therefore, many security companies are developing internal data leakage prevention techniques such as data leakage protection (DLP), digital right management (DRM), and system access control, etc. However, these techniques cannot effectively block the leakage of internal information by insiders who have a legitimate access authorization. The security system does not easily detect cases which a legitimate insider changes, deletes, and leaks data stored on the server. Therefore, we focused on the insider as the detection target to address this security weakness. In other words, we switched the detection target from objects (internal information) to subjects (insiders). We concentrated on biometrics signals change when an insider conducts abnormal behavior. When insiders attempt to leak internal information, they appear to display abnormal emotional conditions due to tension, agitation, and anxiety, etc. These conditions can be detected by the changes of biometrics signals such as pulse, temperature, and skin conductivity, etc. We carried out experiments in two ways in order to verify the effectiveness of the emotional recognition technology based on biometrics signals. We analyzed the possibility of internal information leakage detection using an emotional recognition technology based on biometrics signals through experiments.

• Journal of the Korea Safety Management & Science
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• v.17 no.2
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• pp.129-137
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• 2015

This study is in regard to the gas detection system and gas detection method utilizing smart phone. This study includes; 1) the sensor module attached to the smart phone to detect and measure flammable gas or toxic gas; and 2) gas detection APP which is installed inside the smart phone and recognizes the user information and location information automatically by reading RFID tag indicating the user or the location to detect gas through the contact area where RFID and blue tooth reader is installed inside of the above mentioned smart phone, and then measures the combustible gas or toxic gas by operating above mentioned sensor module and obtains the data thus measured, and above mentioned smart phone is characterized by its transmission of the above mentioned user information, location information and measured data which are obtained by above mentioned gas detecting APP to operation server via communication network. With this, reliability for the location detecting gas by the user, the result of the measurement, etc. can be secured. Furthermore, this provides the effect of preventing artificial manipulation at the time of input which is associated with the identification of the user to be measured by utilizing removable sensor module and application or the mistake resulted from wrong input by the user. In addition, by transmitting the measured data from the sensor module carrying out gas detection to operation server, this provides the effect of making it possible to process the data thus collected to a specialized data for combustible gas or toxic gas.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1544-1553
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• 2018

Recently internal information leakage in industries is severely increasing in spite of industry security policy. Thus, it is essential to prepare an information leakage prevention measure by industries. Most of the leaks result from the insiders, not from external attacks. In this paper, a real-time internal information leakage prevention system via both storage and network is implemented in order to protect confidential file leakage. In addition, a Hadoop-based user behavior analysis and statistics system is designed and implemented for storing and analyzing information log data in industries. The proposed system stores a large volume of data in HDFS and improves data processing capability using RHive, consequently helps the administrator recognize and prepare the confidential file leak trials. The implemented audit system would be contributed to reducing the damage caused by leakage of confidential files inside of the industries via both portable data media and networks.