• Review of KIISC
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• v.32 no.4
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• pp.7-17
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• 2022

운전자 보조 시스템을 통한 차량의 전자적인 제어를 위하여, 최근 차량에 탑재된 전자 제어 장치 (ECU; Electronic Control Unit)의 개수가 급증하고 있다. ECU는 효율적인 통신을 위해서 차량용 내부 네트워크인 CAN(Controller Area Network)을 이용한다. 하지만 CAN은 기밀성, 무결성, 접근 제어, 인증과 같은 보안 메커니즘이 고려되지 않은 상태로 설계되었기 때문에, 공격자가 네트워크에 쉽게 접근하여 메시지를 도청하거나 주입할 수 있다. 악의적인 메시지 주입은 차량 운전자 및 동승자의 안전에 심각한 피해를 안길 수 있기에, 최근에는 주입된 메시지를 식별하기 위한 침입 탐지 시스템(IDS; Intrusion Detection System)에 대한 연구가 발전해왔다. 특히 최근에는 AI(Artificial Intelligence) 기술을 이용한 IDS가 다수 제안되었다. 그러나 제안되는 기법들은 특정 공격 데이터셋에 한하여 평가되며, 각 기법에 대한 탐지 성능이 공정하게 평가되었는지를 확인하기 위한 평가 프레임워크가 부족한 상황이다. 따라서 본 논문에서는 machine learning/deep learning에 기반하여 제안된 차랑용 IDS 5가지를 선정하고, 기존에 공개된 데이터셋을 이용하여 제안된 기법들에 대한 비교 및 평가를 진행한다. 공격 데이터셋에는 CAN의 대표적인 4가지 공격 유형이 포함되어 있으며, 추가적으로 본 논문에서는 메시지 주기 유형을 활용한 공격 유형을 제안하고 해당 공격에 대한 탐지 성능을 평가한다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.213-220
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• 2023

Face recognition is a very important process in video monitoring and is a type of biometric technology. It is mainly used for identification and security purposes, such as ID cards, licenses, and passports. The recognition process has many variables and is complex, so development has been slow. In this paper, we proposed a face recognition method using CNN, which has been re-examined due to the recent development of computers and algorithms, and compared with the feature comparison method, which is an existing face recognition algorithm, to verify performance. The proposed face search method is divided into a face region extraction step and a learning step. For learning, face images were standardized to 50×50 pixels, and learning was conducted while minimizing unnecessary nodes. In this paper, convolution and polling-based techniques, which are one of the deep learning technologies, were used for learning, and 1,000 face images were randomly selected from among 7,000 images of Caltech, and as a result of inspection, the final recognition rate was 98%.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.139-140
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• 2009

양성자 기반 공학 기술 개발 사업단은 20MeV 양성자 가속기를 운전 중이며, 진공, 빔 계측, 전원 등의 장치에 대한 제어 시스템을 개발 운영 중이다. 대형 입자 가속기를 위한 제어 시스템의 전체 네트워크는 사용자 인터페이스와 제어계가 단일 네트워크를 공유하고 있으며 EPICS(Experimental Physics and Industrial Control System) CA(Channel Access)통신을 이용하여 데이터를 상호 교환한다. 그러나 단일 네트워크를 사용함으로서 관리자만이 제어해야 할 데이터는 많은 클라이언트에게 노출되는 문제점이 있다. 그러므로 클라이언트의 접근을 제어하여 제어계로부터 전달되는 신호들의 안정성과 보안성을 유지할 수 있는 방법이 요구된다. 본 논문에서는 제어시스템에 보안성과 안정성을 유지하기 위하여 클라이언트를 Control Network, 제어계를 Machine Network로 분산시키고 통신 중계기 역할을 하는 CA Gateway를 적용하여 클라이언트에게 접근 권한을 주는 기법을 제시하고 구현 및 그 결과를 보고한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.494-510
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• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.919-926
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• 2022

Fertigation, which has been introduced in agricultural fields since 1990, has been widely practiced in upland fields as well as in plastic film houses as part of the crop production system. In accordance with demands in the agricultural sector, a huge number of scientific studies on fertigation have been conducted worldwide. Moreover, with a combination of advanced technologies such as big-data, machine learning, etc., fertigation is positioned as an indispensable tool to achieve sustainable crop production and to enhance nutrient and water use efficiency. In this review, we focused on providing valuable information in terms of crop production and nutrient/water use efficiency. A variety of fertigation studies have described that enhancement of crop production did not differ relative to conventional method or slightly increased. In contrast, fertigation significantly improved nutrient/water use efficiency, with a reduction in use ranging from 20 to 50%. Water-soluble organic resources such as livestock manure and agricultural byproducts also have been identified as useful resources like chemical fertilizers. Furthermore, the initial irrigation point was generally recommended in a range of -10 - -40 kPa, although the point differed according to the crop and crop growth stage. From this review, we suggest that fertigation, which is closely integrated with advanced technology, could be a leading technology to attain not only food security but also carbon neutrality via improvement of nutrient/water use efficiency.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.61-72
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• 2020

The fear of crime, discussed in the early 1960s in the United States, is a psychological response, such as anxiety or concern about crime, the potential victim of a crime. These anxiety factors lead to the burden of the individual in securing the psychological stability and indirect costs of the crime against the society. Fear of crime is not a good thing, and it is a part that needs to be adjusted so that it cannot be exaggerated and distorted by the policy together with the crime coping and resolution. This is because fear of crime has as much harm as damage caused by criminal act. Eric Pawson has argued that the popular impression of violent crime is not formed because of media reports, but by official statistics. Therefore, the police should watch and analyze news related to fear of crime to reduce the social cost of fear of crime and prepare a preemptive response policy before the people have 'fear of crime'. In this paper, we propose a deep - based news classification system that helps police cope with crimes related to crimes reported in the media efficiently and quickly and precisely. The goal is to establish a system that can quickly identify changes in security issues that are rapidly increasing by categorizing news related to crime among news articles. To construct the system, crime data was learned so that news could be classified according to the type of crime. Deep learning was applied by using Google tensor flow. In the future, it is necessary to continue research on the importance of keyword according to early detection of issues that are rapidly increasing by crime type and the power of the press, and it is also necessary to constantly supplement crime related corpus.

• Journal of the Korean housing association
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• v.18 no.1
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• pp.61-72
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• 2007

The purposes of this study were to summarize the concept of well-being and well-being apartment, to grasp the present condition of apartments which were introduced with well-being elements, and to find out the consumer preferences on well-being elements for apartment planning. Library and internet surveys were performed to summarize the concept of well-being and well-being apartment and to grasp the present condition of apartments which were introduced with well-being elements. Questionnaire survey was carried out from 2nd to 22nd of June 2005, to investigate the preferences on well-being elements for apartment planning. The respondents were 250 residents who are from thirties to fifties and living in urban area. As results, respondents think that 'living for health of body and mind' about concept of well-being and 'certificated apartments by green building rating system' or 'apartments introduced ecological factor' about concept of well-being apartment. They answered that 'yes' about 'Do you have intention to buy well-being apartment?'. The elements in aspect of complex planning having the preference were revealed that promenade for complex design, ecological garden or walking space for landscape design, outdoor exercise space for outdoor design, and security system for foundation equipment. The elements having the preference in aspect of public facilities were fitness room for sports & health facility and study room for cultural facility. The preferred elements in aspect of building and unit design were roof garden for building design, multi-functional room for unit floor plan, natural surface material for interior surface, ventilation system for indoor environment, control system for home automation, and food waste machine for home electronics.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.123-129
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• 2014

In accordance with the development of various convergence devices, cameras are being used in many types of the systems such as security system, driver assistance device and so on, and a lot of people are exposed to these system. Therefore the system should be able to recognize the human behavior and support some useful functions with the information that is obtained from detected human behavior. In this paper we use a machine learning approach based on 2D image and propose the human behavior pattern recognition methods. The proposed methods can provide valuable information to support some useful function to user based on the recognized human behavior. First proposed one is "phone call behavior" recognition. If a camera of the black box, which is focused on driver in a car, recognize phone call pose, it can give a warning to driver for safe driving. The second one is "looking ahead" recognition for driving safety where we propose the decision rule and method to decide whether the driver is looking ahead or not. This paper also shows usefulness of proposed recognition methods with some experiment results in real time.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.896-905
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• 2021

In the field of SIEM(Security information and event management), many studies try to use a feedback system to solve lack of completeness of training data and false positives of new attack events that occur in the actual operation. However, the current feedback system requires too much human inputs to improve the running model and even so, those feedback from inexperienced analysts can affect the model performance negatively. Therefore, we propose "active model improving feedback technology" to solve the shortage of security analyst manpower, increasing false positive rates and degrading model performance. First, we cluster similar predicted events during the operation, calculate feedback priorities for those clusters and select and provide representative events from those highly prioritized clusters using XAI (eXplainable AI)-based event visualization. Once these events are feedbacked, we exclude less analogous events and then propagate the feedback throughout the clusters. Finally, these events are incrementally trained by an existing model. To verify the effectiveness of our proposal, we compared three distinct scenarios using PKDD2007 and CSIC2012. As a result, our proposal confirmed a 30% higher performance in all indicators compared to that of the model with no feedback and the current feedback system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.175-184
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• 2009

We are entering into a era of enterprise computing that is characterized by an emphasis on broadband convergence, knowledge s haring, and calm services. Some people refer to this as the "ubiquitous" computing because its focus is on a high degree of connectivity between a company and its customers, suppliers, and channel partners. Ubiquitous computing technology, "RF" stands for "radio frequency"; the "ID" means "identifer". The tag itself of a computer chip and an antenna. The shortest metaphor is that RFID is like a bar-code but is read with an electromagnetic field rather than by a laser beam. Much has already been written about the use of RFID. But there is no has written about the use of RFID in the convention industry. Therefore this study have specific objectives as follows. 1. To give details on the use of RFID in convention. 2. To introduces the key concepts behind RFID technology. 3. To identify advantage & disadvantage of RFID technology using a BEXCO CASE study. 4. To study on ubiquitous convention using RFID and effective operation methods such as entrance identification system, session management, machine management, CRM management, visitor management, and contents management. This results provide into the current status of ubiquitous computing technology in convention industries. Specific advantages by using ubiquitous computing technology(RFID) are one-stop differentiate service, wireless internet service, use of visitor management system, entrance by tag, and U-logistics. On other side, disadvantages are security, stabilization of RFID system, higher price of RFID tag, and commercial scale. Convention by using of RFID technology is currently at an early stage. Convention company as BEXCO need to have the capabilities to adapt, to customize, to commercialize, and to modify technology to suit our circumstances.