• International journal of advanced smart convergence
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• v.4 no.1
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• pp.1-10
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• 2015

Automotive Theft has been an obstinate problem around the world. Design and manufacture of anti-theft systems have become more and more complex due to the rise in complexity of theft in the system. Most of the anti-theft systems available in the market, are the alarm types which audibly deter some thieves away but do not prevent one's car from being stolen and even are not good enough to meet the growing complexity of theft in the country. This paper presents a simple and an efficient anti-theft system which provides improved security by the use of efficient access mechanisms and immobilization systems. This security system can immobilise an automobile and its key auto systems through remote control when it is stolen. It hence deters thieves from committing the theft. It also effectively prevents stealing of key auto systems for reselling by introducing four layers of security features written in the form of firmware and embedded on the Electronic Control Units (ECUs). The particulars of system design and operation are defined in the paper. The experimental outcomes show that this system is practicable and the owner can steadily control his vehicle within a few seconds.

• Journal of Korea Game Society
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• v.11 no.6
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• pp.81-93
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• 2011

Identity theft happens frequently in popular MMORPG(Massively Multi-player Online Role Playing Games) where profits can be gained easily. In spite of the importance of security about identity theft in MMORPG, few methods to prevent and detect identity theft in online games have been proposed. In this study, we investigate real identity theft cases of an online game and define the representative patterns of identity theft as the speedy type, cautious type, and bold type. We then propose the automatic identity theft detection model based on the multi-class classification. We verify the system with one of the leading online games in Korea. The multi-class detection model outperforms the existing binary-class one(hacked or not).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.586-594
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• 2018

In this paper, we propose a livestock theft detection system through moving object classification and tracking method. To do this, first, we extract moving objects using GMM(Gaussian Mixture Model) and RGB background modeling method. Second, it utilizes a morphology technique to remove shadows and noise, and recognizes moving objects through labeling. Third, the recognized moving objects are classified into human and livestock using skeletal features and color similarity judgment. Fourth, for the classified moving objects, CAM (Continuously Adaptive Meanshift) Shift and Kalman Filter are used to perform tracking and overlapping judgment, and risk is judged to generate a notification. Finally, several experiments demonstrate the feasibility and applicability of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.578-585
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• 2018

In this paper, we propose a classification and theft detection system for human and livestock for various moving objects in a barn. To do this, first, we extract the moving objects using the GMM method. Second, the noise generated when extracting the moving object is removed, and the moving object is recognized through the labeling method. And we propose a method to classify human and livestock using model formation and color for the unique form of the detected moving object. In addition, we propose a method of tracking and overlapping the classified moving objects using Kalman filter. Through this overlap determination method, an event notifying a dangerous situation is generated and a theft determination system is constructed. Finally, we demonstrate the feasibility and applicability of the proposed system through several experiments.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.51-60
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• 2016

The incidents of massive personal information being leaked are occurring continuously over recent years. Personal information leaked outside is used for an illegal use of other's name and account theft. Especially it is happening on online games whose virtual goods, online game money and game items can be exchanged with real cash. When we research the real identity theft cases that happened in an online game, we can see that they happen massively in a short time. In this study, we define the characteristics of the mass attacks of the automated identity theft cases that occur in online games. Also we suggest a system to detect and prevent identity theft attacks in real time.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.445-446
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• 2012

The growth in number and capacity of smart devices such as GPS enabled smart phones and PDAs present an unparalleled opportunity for diverse areas of life. In this paper we propose an approach for vehicle theft protection using GPS based trajectory anomaly detection. The detailed methodology of the proposed system is briefly described in this paper.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.431-433
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• 2021

In this paper, abnormal behavior monitoring system using YOLO AI platform was implemented and had superior response characteristics compared to the conventional monitoring system using two-shot detection by using one-shot detection of YOLO system. The YOLO platform was trained using image dataset composed of abnormal behaviors such as assault, theft, and arson. The abnormal behavior monitoring system consists of client and server and can be applicable to smart cities to solve various crime problems if it is commercialized.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.2
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• pp.339-344
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• 2013

Airport security activities to ensure the airport's facilities and assets from crime, such as theft, to prevent. security systems that support these activities efficiently so you can be called. security center systems, CCTV systems, access control systems, intrusion detection systems, security communication systems, warning broadcasting system, network security systems, perimeter intrusion detection systems, search systems, and information security systems for the security system will be examined.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.119-125
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• 2021

As the electricity grid systems has been intelligent with the development of ICT technology, power consumption information of users connected to the grid is available to acquired and analyzed for the power utilities. In this paper, the energy theft problem is solved by feature selection methods, which is emerging as the main cause of economic loss in smart grid. The data preprocessing steps of the proposed system consists of five steps. In the feature selection step, features are selected using analysis of variance and mutual information (MI) based method, which are filtering-based feature selection methods. According to the simulation results, the performance of support vector machine classifier is higher than the case of using all the input features of the input data for the case of the MI based feature selection method.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.1-10
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• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.