• Annual Conference of KIPS
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• 2022.11a
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• pp.1068-1070
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• 2022

우리나라의 무역 활동을 처리하는 항만은 국가 주요시설로 보안에 만전을 기하고 있다. 그러나 항만의 면적이 넓고 복잡하기 때문에 사각지대가 존재하고 사각지대에서의 불법행위 단속 건수는 매년 증가하고 있다. 이에 항만의 보안 강화를 위한 대책이 필요하다. 본 논문은 항만의 상황을 이동형 CCTV에 부착된 IoT 센서들로 인식하여 YOLOv5 딥러닝 모델로 분석한 후 웹 대시보드에 시각화하는 항만 보안 시스템을 제안한다. 이동형 CCTV는 특정 위치로 직접 이동할 수 있어 거리에 따라 해상도가 낮아지는 기존 CCTV의 단점을 보완할 수 있다. 또한 해당 시스템은 주변에서 쉽게 구할 수 있는 장비들과 오픈소스 라이브러리를 활용하기 때문에 다른 보안장비들에 비해 효율적인 비용으로 높은 보안 효과를 얻을 수 있다는 강점을 지닌다. 본 시스템은 항만시설뿐 아니라 군사시설, 물류시설 등 보안을 중요시하는 다른 분야에 확대 적용될 수 있다는 점에서 의의가 있다.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.458-463
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• 2023

This study presents a paper on the implementation of a Raspberry Pi-based educational smart farm system. It confirms that in a real smart farm environment, the control of temperature, humidity, soil moisture, and light intensity can be smoothly managed. It also includes remote monitoring and control of sensor information through a web service. Additionally, information about intruders collected by the Pi camera is transmitted to the administrator. Although the cost of existing smart farms varies depending on the location, material, and type of installation, it costs 400 million won for polytunnel and 1.5 billion won for glass greenhouses when constructing 0.5ha (1,500 pyeong) on average. Nevertheless, among the problems of smart farms, there are lax locks, malfunctions to automation, and errors in smart farm sensors (power problems, etc.). We believe that this study can protect crops at low cost if it is complementarily used to improve the security and reliability of expensive smart farms. The cost of using this study is about 100,000 won, so it can be used inexpensively even when applied to the area. In addition, in the case of plant cultivators, cultivators with remote control functions are sold for more than 1 million won, so they can be used as low-cost plant cultivators.

• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.256-264
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• 2024

In the last 10 years, AI has made rapid progress, and image classification, in particular, are showing excellent performance based on deep learning. Nevertheless, due to the nature of deep learning represented by a black box, it is difficult to actually use it in critical decision-making situations such as national defense, autonomous driving, medical care, and finance due to the lack of explainability of judgement results. In order to overcome these limitations, in this study, a model description algorithm capable of local interpretation was applied to the inception network-derived AI to analyze what grounds they made when classifying national defense data. Specifically, we conduct a comparative analysis of explainability based on confidence values by performing LIME analysis from the Inception v2_resnet model and verify the similarity between human interpretations and LIME explanations. Furthermore, by comparing the LIME explanation results through the Top1 output results for Inception v3, Inception v2_resnet, and Xception models, we confirm the feasibility of comparing the efficiency and availability of deep learning networks using XAI.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.3
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• pp.13-23
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• 2024

For the purpose of predicting credit card customer churn accurately through data analysis Detecting and tracking objects in continuous video is essential in self-driving cars, security and surveillance systems, sports analytics, medical image processing, and more. Correlation tracking methods such as Normalized Cross Correlation(NCC) and Sum of Absolute Differences(SAD) are used as an effective way to measure the similarity between two images. NCC, a representative correlation tracking method, has been useful in real-time environments because it is relatively simple to compute and effective. However, correlation tracking methods are sensitive to rotation and size changes of objects, making them difficult to apply to real-time changing videos. To overcome these limitations, this paper proposes an object tracking method using the Histogram of Oriented Gradients(HOG) feature to effectively obtain object data and the Convolution Neural Network(CNN) algorithm. By using the two algorithms, the shape and structure of the object can be effectively represented and learned, resulting in more reliable and accurate object tracking. In this paper, the performance of the proposed method is verified through experiments and its superiority is demonstrated.

• ETRI Journal
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• v.38 no.4
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• pp.735-745
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• 2016

The Paris agreement at the 21st Conference of the Parties (COP21) emphasizes the reduction of greenhouse gas emissions and increase in energy consumption in all areas. Thus, an important aspect is energy saving in buildings where the lighting is a major component of the electrical energy consumption. This paper proposes a building energy management system employing visible light communication (VLC) based on LED lighting. The proposed management system has key characteristics including personalization and localization by utilizing such VLC advantages as secure communication through light and location-information transmission. Considering the efficient implementation of an energy-consumption adjustment using LED luminaires, this paper adopts variable pulse position modulation (VPPM) as a VLC modulation scheme with simple controllability of the dimming level that is capable of providing a full dimming range. This paper analyzes the VPPM performances according to variable dimming for several schemes, and proposes a VPPM demodulation architecture based on dimming-factor acquisition, which can obtain an improved performance compared to a 2PPM-based scheme. In addition, the effect of a dimming-factor acquisition error is analyzed, and a frame format for minimizing this error effect is proposed.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1063-1066
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• 2005

In this paper, we developed the UHF RFID system for EPC Class1 Generation 2 specification. The RFID is a technique of identifying an object using radio frequency transmission. UHF band RFID system communication between Reader and Tag for provide the power to tag when be active when reader’s command finished the transfer. RFID technology can be applied to the supply chain, security, logistics industry and etc. Especially, UHF RFID is worth noticing because of its relatively long identification rage and commercial UHF RFID systems are under development.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.31-36
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• 2016

User authentication is the first step to network security. There are lots of authentication types, and more than one authentication method works together for user's authentication in the network. Except for biometric authentication, most authentication methods can be copied, or someone else can adopt and abuse someone else's credential method. Thus, more than one authentication method must be used for user authentication. However, more credential makes system degrade and inefficient as they log on the system. Therefore, without tradeoff performance with efficiency, this research proposed user's behavior based authentication for secure communication, and it will improve to establish a secure and efficient communication.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.459-468
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• 2018

Cyberattacks on information systems used by applications related to weapon system and organizations associated with national defense put national security at risk. To reduce these threats, continuous efforts such as applying secure coding from the development stage or managing detected vulnerabilities systematically are being made. It also analyzes and detects vulnerabilities by using various analysis tools, eliminates at the development stage, and removes from developed applications. However, vulnerability analysis tools cause problems such as undetected, false positives, and overdetected, making accurate vulnerability detection difficult. In this paper, we propose a new vulnerability detection method to solve these problems, which can assess the risk of certain applications and create and manage secured application with this data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2713-2717
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• 2011

To transmit data from straggling sensors in water-processing basic industries etc., used Binary-CDMA system has safety voluntarily. But Binary-CDMA is necessity that react very sensitively in environment change as defense about hacking and cracking of various way that change suddenly. Therefore, this paper is that see added cryptographic algorithm for safety and easy update on correlator that a bottle-neck phenomenon is happened in Binary-CDMA to solve problem that is such. Added cryptographic algorithm does to communicate safe information in channel that is not safe as that achieve 1:1 confrontation for sensors by symmetric cryptographic algorithm.