• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.13-22
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• 2021

Recently network based attack technologies are rapidly advanced and intelligent, the limitations of existing signature-based intrusion detection systems are becoming clear. The reason is that signature-based detection methods lack generalization capabilities for new attacks such as APT attacks. To solve these problems, research on machine learning-based intrusion detection systems is being actively conducted. However, in the actual network environment, attack samples are collected very little compared to normal samples, resulting in class imbalance problems. When a supervised learning-based anomaly detection model is trained with such data, the result is biased to the normal sample. In this paper, we propose to overcome this imbalance problem through One-Class Anomaly Detection using an auto encoder. The experiment was conducted through the NSL-KDD data set and compares the performance with the supervised learning models for the performance evaluation of the proposed method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.144-155
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• 2019

One of the issues of the automotive industry today is autonomous driving vehicles. In order to achieve level 3 or higher as defined by SAE International, harmonization of autonomous driving technology and connected technology is essential. Current vehicles have new features such as autonomous driving, which not only increases the number of electrical components, but also the amount and complexity of software. As a result, the attack surface, which is the access point of attack, is widening, and software security vulnerabilities are also increasing. However, the reality is that the essential security requirements for vehicles are not defined. In this paper, based on real attacks and vulnerability cases and trends, we identify the assets in the in-vehicle network and derive the threats. We also defined the security requirements and derived essential security requirements that should be applied at least to the safety of the vehicle occupant through risk analysis.

• Genes and Genomics
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• v.40 no.12
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• pp.1363-1371
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• 2018

One of the main concerns in biology is extracting sophisticated features from DNA sequence for gene interaction determination, receiving a great deal of researchers' attention. The epigenetic modifications along with their patterns have been intensely recognized as dominant features affecting on gene expression. However, studying sequenced-based features highly correlated to this key element has remained limited. The main objective in this research was to propose a new feature highly correlated to epigenetic modifications capable of classification of genes. In this paper, classification of 34 genes in PPAR signaling pathway associated with muscle fat tissue in human was performed. Using different statistical outlier detection methods, we proposed that 5-mers highly correlated to epigenetic modifications can correctly categorize the genes involved in the same biological pathway or process. Thirty-four genes in PPAR signaling pathway were classified via applying a proposed feature, 5-mers strongly associated to 17 different epigenetic modifications. For this, diverse statistical outlier detection methods were applied to specify the group of thoroughly correlated genes. The results indicated that these 5-mers can appropriately identify correlated genes. In addition, our results corresponded to GeneMania interaction information, leading to support the suggested method. The appealing findings imply that not only epigenetic modifications but also their highly correlated 5-mers can be applied for reconstructing gene regulatory networks as supplementary data as well as other applications like physical interaction, genes prioritization, indicating some sort of data fusion in this analysis.

• The Korean Journal of Archival Studies
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• no.59
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• pp.79-127
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• 2019

As research of oral history improved, not only public institutions but also private sectors actively collected and managed too. Despite these demands, however, there was not enough systematic analysis of systems targeted at oral archives. In this study, which started from this problem equation, the characteristics of oral archives and systems were analyzed and the institution that manages and serves current oral archives was selected to derive problems. It also analyzed the information required by the Presidential Archives, which collects oral archives but plans to build a system design. I will present suggestions for the establishment of a systematic oral archives management system focusing on problems and improvement measures that arise in the process.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• Database Research
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• v.34 no.3
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• pp.86-98
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• 2018

Point-of-Interest(POI) recommendation systems suggest the most interesting POIs to users considering the current location and time. With the rapid development of smartphones, internet-of-things, and location-based social networks, it has become feasible to accumulate huge amounts of user POI visits. Therefore, instant recommendation of interesting POIs at a given time is being widely recognized as important. To increase the performance of POI recommendation systems, several studies extracting users' POI sequential preference from POI check-in data, which is intended for implicit feedback, have been suggested. However, when constructing a model utilizing sequential preference, the model encounters possibility of data distortion because of a low number of observed check-ins which is attributed to intensified data sparsity. This paper suggests refinement of temporal intervals based on data confidence. When building a POI recommendation system using temporal intervals to model the POI sequential preference of users, our methodology reduces potential data distortion in the dataset and thus increases the performance of the recommendation system. We verify our model's effectiveness through the evaluation with the Foursquare and Gowalla dataset.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.623-631
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• 2020

An electromagnetic pulse (EMP) attack on a nuclear weapon or the airlift of an electronic bomb affects weapons systems, information devices, wired and wireless communication equipment, and power supply equipment. It can lead to confusion on the battlefield. The current standards for EMP protection when applied to the military are centered on fixed and mobile facilities and equipment. It is, however, important to study EMP protection for a single tactical unit centered on the weapon system. In this study, EMP protection standards were established for command and control, maneuvering and firepower systems vulnerable to EMPs, focusing on battle tanks with mobility, firepower, and shock force. Also, specific development plans for EMP protection capabilities are proposed, including the shielding and blocking of EMPs. Through the study, the Korean government intends to ensure a unit's command and control under an EMP attack as well as preserve the viability of a unit's personnel and guarantee the conditions for the execution of a mission.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• Research in Community and Public Health Nursing
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• v.32 no.1
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• pp.1-11
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• 2021

Purpose: This qualitative study is aimed to explore the factors in the aspect of the community that made it difficult for field experts to conduct counseling and case management. Methods: A total of four focus group interviews composed of 15 field experts including nurses were conducted. Results: A theme, six categories and 22 subcategories were derived. As for the theme, it was found that legal, educational, and environmental systems reflecting non-suicidal self-injury of the characteristics in adolescents were insufficient. In the legal aspect, the defect of the parental education legal system, the reality of having to rely on parental consent when supporting adolescents with non-suicidal self-injury; in the educational aspect, the lack of manuals and education for counseling and case management for adolescents with non-suicidal self-injury; in the environmental aspects, the defect of economic burden and support, a lack of information systems for various organizations in the local community, absence of a dedicated support system for adolescent with non-suicidal self-injury and a lack of human and physical resources. Conclusion: Based on the results of this study, there needs to be a responsible institution that can comprehensively manage the non-suicidal self-injury of adolescents, and efforts to develop the competence of community nurses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.702-708
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• 2020

Recently, as LED display systems have become larger, research on effective power control methods for the systems has been in progress. This paper proposes a power control method to minimize power loss due to the difference in LED characteristics for each channel of a backlight unit (BLU) system. The proposed LED driver IC has a power optimization function and detects the minimum headroom voltage for constant current operation of all channels and linearly controls the DC-DC converter output. Thus, it minimizes power consumption due to unnecessary additional voltage. In addition, it does not require a voltage sensing comparator or a voltage generation circuit for each channel. This has a great advantage in reducing the chip size and for stabilization when implementing an integrated circuit. In order to verify the proposed function, an IC was designed using Cadence and Synopsys' design tools, and it was fabricated with a Magnachip 0.35um 5V/40V CMOS process. The experiments confirmed that the proposed power control method controls the minimum required voltage of the BLU system.