• Journal of Information Processing Systems
• /
• v.15 no.3
• /
• pp.520-537
• /
• 2019

This paper proposes a system that can detect the data leakage pattern using a convolutional neural network based on defining the behaviors of leaking data. In this case, the leakage detection scenario of data leakage is composed of the patterns of occurrence of security logs by administration and related patterns between the security logs that are analyzed by association relationship analysis. This proposed system then detects whether the data is leaked through the convolutional neural network using an insider malicious behavior graph. Since each graph is drawn according to the leakage detection scenario of a data leakage, the system can identify the criminal insider along with the source of malicious behavior according to the results of the convolutional neural network. The results of the performance experiment using a virtual scenario show that even if a new malicious pattern that has not been previously defined is inputted into the data leakage detection system, it is possible to determine whether the data has been leaked. In addition, as compared with other data leakage detection systems, it can be seen that the proposed system is able to detect data leakage more flexibly.

• Journal of Power System Engineering
• /
• v.13 no.5
• /
• pp.59-66
• /
• 2009

High temperature and pressure valves in power plant have been used for fluid flowing and leakage occurred owing to valve internal damage such as disc wear, crack and inserting of foreign objects etc. in these valves. Recently, multi-measuring technique applied both ultrasonic and acoustic method have been used for evaluation of valve internal leakage in order to raise measurement reliability. Therefore, we have performed various leakage tests using ultrasonic and acoustic measuring system and acquired leakage data for the various leakage conditions. In this study, we developed the estimation method of regression model through leakage data, and expectation method for valve opening ratio, which is directly proportion to leakage rate, using the established estimation model from the measured data, valve size and fluid pressure so as to enhance data reliability. As a result of this study, it was founded that expectation method of leakage rate by statistical analysis method is appropriate to valve leakage evaluation.

• Journal of Korean Society of Water and Wastewater
• /
• v.35 no.2
• /
• pp.121-133
• /
• 2021

This study set up the estimates of leakage management efficiency evaluation and leakage management goal that could be used in local water distribution networks efficiency business and modernization business. The data were analyzed using data envelopment analysis and multiple regression analysis. To this end, with leakage management input indices concerning leakage reduction activities (e.g., aged pipe replacement, water meter replacement, leakage restoration, and leakage detection) and leakage management calculation indices (e.g., the increase of revenue water ratio and the reduction of leakage ratio), the data on 22 K-water consignment local water supply systems were analyzed for the years from 2004 through 2018. Using the results of efficiency analysis by data envelopment analysis, the other DMUs (Decision Making Unit) benchmarked the DMU with the highest efficiency to maximize the leakage management efficiency for all DMUs. Through this, leakage management goal estimates were drawn with the input indices of four leakage reduction activities and calculation indices of the increase of revenue water ratio and the reduction of leakage ratio by multiple regression analysis for each group based on the revenue water ratio and leakage ratio. The correlation coefficients of the leakage management goal estimate for the criteria for the revenue water ratio amounted to 0.553 and 0.771. The correlation coefficients of the leakage management goal estimate for the criteria for leakage ratio were 0.397 and 0.865. Accordingly, we estimated the quantity and priority of four leakage reduction activities for the target leakage ratio and revenue water ratio.

• International Journal of Fluid Machinery and Systems
• /
• v.3 no.3
• /
• pp.253-259
• /
• 2010

Leakage characteristics of two labyrinth seals with different configurations (straight vs stepped) were investigated. Leakage flows were predicted by computational fluid dynamics (CFD) for the two configurations and compared with test data. A semi-analytical leakage prediction tool was also tried to predict the leakage. It was confirmed that the CFD gives quite good agreements with test data. The analytical tool also yielded similar leakage behaviors with test results, but the overall agreement with test data was not as good as that of the CFD. The effect of flow direction in the stepped seal on leakage flow was examined. The dependence of leakage performance, in terms of flow function, on the seal clearance size was investigated. Flow function decreased with decreasing clearance in the straight seal, while the trend was reversed in the stepped seal.

• Nuclear Engineering and Technology
• /
• v.55 no.4
• /
• pp.1528-1539
• /
• 2023

Since the leakage of sodium in an SFR (sodium-cooled fast reactor) causes an explosion upon reaction with air and water, sodium leakages represent an important safety issue. In this study, a novel technique for improving the reliability of sodium leakage detection applying DDVR (dynamic data validation and reconciliation) is proposed and verified to resolve this technical issue. DDVR is an approach that aims to improve the accuracy of a target system in a dynamic state by minimizing random errors, such as from the uncertainty of instruments and the surrounding environment, and by eliminating gross errors, such as instrument failure, miscalibration, or aging, using the spatial redundancy of measurements in a physical model and the reliability information of the instruments. DDVR also makes it possible to estimate the state of unmeasured points. To validate this approach for supporting sodium leakage detection, this study applies experimental data from a sodium leakage detection experiment performed by the Korea Atomic Energy Research Institute. The validation results show that the reliability of sodium leakage detection is improved by cooperation between DDVR and hardware measurements. Based on these findings, technology integrating software and hardware approaches is suggested to improve the reliability of sodium leakage detection by presenting the expected true state of the system.

• The Journal of Information Systems
• /
• v.27 no.1
• /
• pp.171-191
• /
• 2018

Purpose Corporate technology leakage is not only monetary loss, but also has a negative impact on the corporate image and further deteriorates sustainable growth. In particular, since SMEs are highly dependent on core technologies compared to large corporations, loss of technology leakage threatens corporate survival. Therefore, it is important for SMEs to "prevent and protect technology leakage". With the recent development of data analysis technology and the opening of public data, it has become possible to discover and proactively detect companies with a high probability of technology leakage based on actual company data. In this study, we try to construct profiles of enterprises with and without technology leakage experience through profiling analysis using data mining techniques. Furthermore, based on this, we propose a classification model that distinguishes companies that are likely to leak technology. Design/methodology/approach This study tries to develop the empirical model for prevention and protection of technology leakage through profiling method which analyzes each SME from the viewpoint of individual. Based on the previous research, we tried to classify many characteristics of SMEs into six categories and to identify the factors influencing the technology leakage of SMEs from the enterprise point of view. Specifically, we divided the 29 SME characteristics into the following six categories: 'firm characteristics', 'organizational characteristics', 'technical characteristics', 'relational characteristics', 'financial characteristics', and 'enterprise core competencies'. Each characteristic was extracted from the questionnaire data of 'Survey of Small and Medium Enterprises Technology' carried out annually by the Government of the Republic of Korea. Since the number of SMEs with experience of technology leakage in questionnaire data was significantly smaller than the other, we made a 1: 1 correspondence with each sample through mixed sampling. We conducted profiling of companies with and without technology leakage experience using decision-tree technique for research data, and derived meaningful variables that can distinguish the two. Then, empirical model for prevention and protection of technology leakage was developed through discriminant analysis and logistic regression analysis. Findings Profiling analysis shows that technology novelty, enterprise technology group, number of intellectual property registrations, product life cycle, technology development infrastructure level(absence of dedicated organization), enterprise core competency(design) and enterprise core competency(process design) help us find SME's technology leakage. We developed the two empirical model for prevention and protection of technology leakage in SMEs using discriminant analysis and logistic regression analysis, and each hit ratio is 65%(discriminant analysis) and 67%(logistic regression analysis).

• Journal of Korea Water Resources Association
• /
• v.56 no.5
• /
• pp.347-356
• /
• 2023

In Water Distribution Systems (WDSs), can abnormal hydraulic and water quality conditions such as red-water phenomenon and leakage occur. To restore them, data is generated through various meters data to predict and detect. However, in the case of leakage if difficult to detect unless direct exploration is performed. Among them, unreported leakage, are not seen visually and account for the most considerable volumes of leakage, which leads to economic loss. Bur direct exploration is limited through on site conditions such as securing professional manpower. In this paper, leakage volumes and location were randomly generated for the WDS, which was assumed to be calibrated, and it was detected through a deep learning model. For abnormal data generation, the leakage was simulated using the emitter coefficient, and leakage detection was successfully performed through the generated abnormal data and normal data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.3
• /
• pp.558-575
• /
• 2013

The purpose of a data leakage prevention system is to protect corporate information assets. The system monitors the packet exchanges between internal systems and the Internet, filters packets according to the data security policy defined by each company, or discretionarily deletes important data included in packets in order to prevent leakage of corporate information. However, the problem arises that the system may monitor employees' personal information, thus allowing their privacy to be violated. Therefore, it is necessary to find not only a solution for detecting leakage of significant information, but also a way to minimize the leakage of internal users' personal information. In this paper, we propose two models for representing the level of personal information disclosure during data leakage detection. One model measures only the disclosure frequencies of keywords that are defined as personal data. These frequencies are used to indicate the privacy violation level. The other model represents the context of privacy violation using a private data matrix. Each row of the matrix represents the disclosure counts for personal data keywords in a given time period, and each column represents the disclosure count of a certain keyword during the entire observation interval. Using the suggested matrix model, we can represent an abstracted context of the privacy violation situation. Experiments on the privacy violation situation to demonstrate the usability of the suggested models are also presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.217-219
• /
• 2021

With the development of ICT, the amount of data increases, and the technology of storing and processing becomes important. In this study, we study real-time file copy leakage prevention system to prevent leakage of important data in enterprises, public places, etc. As a research method, we propose a system that detects events in real time to prevent data leakage after analyzing data leakage cases and problems. The file leakage prevention system compares and analyzes with the existing EDLP system, and the proposed system reduces load and detects events. Future research requires research on the prevention of leaks through networks and various channels.

• Journal of IKEEE
• /
• v.16 no.3
• /
• pp.265-273
• /
• 2012

Data stability and leakage power dissipation have become a critical issue in scaled SRAM design. In this paper, an advanced 8T SRAM cell improving the read and write stability of data storage elements as well as reducing the leakage current in the idle mode is presented. During the read operation, the bit-cell keeps the noise-vulnerable data 'low' node voltage close to the ground level, and thus producing near-ideal voltage transfer characteristics essential for robust read functionality. In the write operation, a negative bias on the cell facilitates to change the contents of the bit. Unlike the conventional 6T cell, there is no conflicting read and write requirement on sizing the transistors. In the standby mode, the built-in stacked device in the 8T cell reduces the leakage current significantly. The 8T SRAM cell implemented in a 130 nm CMOS technology demonstrates almost 100 % higher read stability while bearing 20 % better write-ability at 1.2 V typical condition, and a reduction by 45 % in leakage power consumption compared to the standard 6T cell. The stability enhancement and leakage power reduction provided with the proposed bit-cell are confirmed under process, voltage and temperature variations.