• International Journal of Computer Science & Network Security
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• 제24권4호
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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.

• 한국통신학회논문지
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• 제38B권7호
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• pp.589-595
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• 2013

최근 보안 관제를 위한 인원부족 및 감시 능력의 한계로 자동화된 지능형 관제 시스템에 대한 요구가 증가하고 있다. 이 논문에서는 지능형 감시시스템의 구축을 위하여 자동화된 객체추출 및 추적 시스템, 그리고 이상행위를 인지하는 이상행위 검출 시스템을 설계하고 구현하였다. 각 모듈은 기존의 연구 결과를 바탕으로 실제 환경에서 적용되고 상용화가 가능하도록 알고리즘의 성능을 높였으며, 구현 후 다양한 테스트를 통해 그 성과를 검증하였다. 특히, 배회 또는 도주와 같은 이상행위의 경우 1초 이내에 검출할 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제9권1호
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• pp.37-43
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• 2004

컴퓨터 네트워크의 확대 및 인터넷 이용의 급속한 증가에 따라 컴퓨터 보안문제가 중요하게 되었다 따라서 침입자들로부터 위험을 줄이기 위해 침입탐지 시스템에 관한 연구가 진행되고 있다. 본 논문에서는 네트워크 기반의 이상 침입 탐지를 위하여 뉴로-퍼지 기법을 적용하고자 한다 불확실성을 처리하는 퍼지 이론을 이상 침입 탐지영역에 도입하여 적용함으로써 오용 탐지의 한계성을 극복하여 알려지지 않은 침입탐지를 하고자 한다.

• 디지털산업정보학회논문지
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• 제8권2호
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• pp.59-65
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• 2012

Emergency medical information center performs role of medical direction about disease consult and pre-hospital emergency handling scheme work to people. Emergency medical information system plays a major role to be decreased mortality and disability of emergency patient by providing information of medical institution especially when emergency patient has appeared. But, various attacks as a hacking have been happened in Emergency medical information system recently. In this paper, we proposed security structure which can protect the system securely by detecting attacks from outside effectively. Intrusion detection was performed using rule based detection technique according to protocol for every packet to detect attack and intrusion was reported to control center if intrusion was detected also. Intrusion detection was performed again using decision tree for packet which intrusion detection was not done. We experimented effectiveness using attacks as TCP-SYN, UDP flooding and ICMP flooding for proposed security structure in this paper.

• 한국정보통신학회논문지
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• 제18권6호
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• pp.1488-1493
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• 2014

전 세계적으로 해운물류 안전 보안체계가 강화됨에 따라 국가물류보안 체계 구축을 위한 해운물류 안전 보안 핵심기술 개발이 이루어지고 있다. 이러한 국제적 정서에 발맞추어, 국내에서도 감마선 핵종을 검출할 수 있는 휴대용 방사선 검출 장치에 대한 관심이 높아지고 있다. 본 논문에서는 Cortex-A9을 이용한 휴대용 방사선 검출장치 플랫폼 검출 성능의 향상을 위한 연구하였다.

• 한국산업정보학회논문지
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• 제8권4호
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• pp.8-16
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• 2003

본 논문에서는 기존의 네트워크 기반의 침입탐지 시스템인 Snort에 존재하는 취약성을 해결하기 위한 방법을 제안한다. 현재 룰 기반의 침입탐지 시스템인 Snort에서는 룰 자체를 보호하기 위한 방법을 제공하지 못한다. 이러한 문제를 해결하기 위해서 본 논문에서는 해쉬함수를 이용하여 룰 자체에 대한 보호를 제공할 수 있는 기법을 제안한다. 이러한 기법을 통하여 룰 자체에 대한 무결성과 기밀성을 제공할 수 있을 것이다.

• ETRI Journal
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• 제43권1호
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• pp.152-162
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• 2021

In the last few years, detection has become a powerful methodology for network protection and security. This paper presents a new detection scheme for data recorded over a computer network. This approach is applicable to the broad scientific field of information security, including intrusion detection and prevention. The proposed method employs bidimensional (time-frequency) data representations of the forms of the short-time Fourier transform, as well as the Wigner distribution. Moreover, the method applies matrix factorization using singular value decomposition and principal component analysis of the two-dimensional data representation matrices to detect intrusions. The current scheme was evaluated using numerous tests on network activities, which were recorded and presented in the KDD-NSL and UNSW-NB15 datasets. The efficiency and robustness of the technique have been experimentally proved.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.571-585
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• 2021

Applications for object detection are expanding as it is automated through artificial intelligence-based processing, such as deep learning, on a large volume of images and videos. High dependence on training data and a non-transparent way to find answers are the common characteristics of deep learning. Attacks on training data and training models have emerged, which are closely related to the nature of deep learning. Privacy, integrity, and robustness for the extracted information are important security issues because deep learning enables object recognition in images and videos. This paper summarizes the security issues that need to be addressed for future applications and analyzes the state-of-the-art security studies related to robustness, privacy, and integrity of object detection for images and videos.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.67-74
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• 2022

One type of network security breach is the availability breach, which deprives legitimate users of their right to access services. The Denial of Service (DoS) attack is one way to have this breach, whereas using the Intrusion Detection System (IDS) is the trending way to detect a DoS attack. However, building IDS has two challenges: reducing the false alert and picking up the right dataset to train the IDS model. The survey concluded, in the end, that using a real dataset such as MAWILab or some tools like ID2T that give the researcher the ability to create a custom dataset may enhance the IDS model to handle the network threats, including DoS attacks. In addition to minimizing the rate of the false alert.