• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1674-1688
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• 2023

The remarkable advancement of quantum steganography offers enhanced security for quantum communications. However, there is a significant concern regarding the potential misuse of this technology. Moreover, the current research on identifying malicious quantum steganography is insufficient. To address this gap in steganalysis research, this paper proposes a specialized quantum steganalysis algorithm. This algorithm utilizes quantum machine learning techniques to detect steganography in general quantum secure communication schemes that are based on pure states. The algorithm presented in this paper consists of two main steps: data preprocessing and automatic discrimination. The data preprocessing step involves extracting and amplifying abnormal signals, followed by the automatic detection of suspicious quantum carriers through training on steganographic and non-steganographic data. The numerical results demonstrate that a larger disparity between the probability distributions of steganographic and non-steganographic data leads to a higher steganographic detection indicator, making the presence of steganography easier to detect. By selecting an appropriate threshold value, the steganography detection rate can exceed 90%.

• 대한산업공학회지
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• 제38권1호
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• pp.46-56
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• 2012

The purpose of virtual metrology (VM) in semiconductor manufacturing is to predict every wafer's metrological values based on its process equipment data without an actual metrology. In this paper, we propose novelty detection-based reliability estimation models for VM in order to support flexible utilization of VM results. Because the proposed model can not only estimate the reliability of VM, but also identify suspicious process variables lowering the reliability, quality control actions can be taken selectively based on the reliance level and its causes. Based on the preliminary experimental results with actual semiconductor manufacturing process data, our models can successfully give a high reliance level to the wafers with small prediction errors and a low reliance level to the wafers with large prediction errors. In addition, our proposed model can give more detailed information by identifying the critical process variables and their relative impacts on the low reliability.

• Transactions on Electrical and Electronic Materials
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• 제13권3호
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• pp.139-143
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• 2012

Optical emission spectroscopy (OES) is often used for real-time analysis of the plasma processes. OES has been suggested as a primary plasma process monitoring tool. It has the advantage of non-invasive in-situ monitoring capability but selecting the proper wavelengths for the analysis of OES data generally relies on empirically established methods. In this paper, we propose a practical method for the selection of OES wavelength peaks for the analysis of plasma etch process and this is done by investigating reactants and by-product gas species that reside in the plasma etch chamber. Wavelength selection criteria are based on the standard deviation and correlation coefficients. Moreover, chemical actinometry is employed for the normalization of the selected wavelengths. We also present the importance of chemical actinometry of OES data for quantitative analysis of plasma. Then, the suggested OES peak selection method is employed.. This method is used to find out the reason behind abnormal etching of PR erosion during a series of $SiO_2$ etch processes using the same recipe. From the experimental verification, we convinced that OES is not only capable for real-time detection of abnormal plasma process but it is also useful for the analysis of suspicious plasma behavior.

• Toxicological Research
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• 제1권1호
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• pp.17-30
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• 1985

Mycotoxins are a group of toxicants giving a risk potential to human health in connection with the daily food intake. Food commodities once contaminated with mycotoxins can not be detoxified by any economic means and prevention was suggested as the only measure. In order to minimize the economic loss and health hazard posed by mycotoxins and toxicoses, systematic and toxicological studies on the subject should be undertaken. Most reports in Korea were concentrated on the mycological studies of relatively easy techniques and the confirmation or quantitation of mycotoxins was rarely done. Research topics to be undertaken in future may be exemplifid below: (1) Establishing assay methods for individual or multi-residue of mycotoxins (2) Monitoring of mycotoxins for suspicious food or feed samples in Korea (3) Epidemiological survey of mycotoxicoses (4) Etiological survey of disease outbreaks associated with mycotoxins (5) Accumulation of testing method and data on the toxicity of mycotoxins (6) Legal regulation to control mycotoxins and development of their detoxification / elimination methods

• Journal of Information Technology Applications and Management
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• 제26권3호
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• pp.29-41
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• 2019

As users' interests of cryptocurrency has been increased, investment volume of it also increases. In the cryptocurrency market, it cannot always be distributed homogenous information to all investors, similar to the stock market because it reflects the characteristics of a market microstructure. Cryptocurrency traders, thus, like stock investors, can experience the information asymmetry in the market and cannot but help to depend on private information. The purpose of this study is to estimate the trading intensity of informed traders and uninformed traders among cryptocurrency investors around the world based on PIN (Probability of Informed Trading). We have an aim to compare the difference of information asymmetry according to the ten types of cryptocurrency. The results of this study are expected to prevent the continuous increase of suspicious transactions related to cryptocurrency and contribute to the development of a sound cryptocurrency market.

• ETRI Journal
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• 제33권5호
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• pp.770-779
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• 2011

Security quad and cube (SQC) is a network attack analyzer that is capable of aggregating many different events into a single significant incident and visualizing these events in order to identify suspicious or illegitimate behavior. A network administrator recognizes network anomalies by analyzing the traffic data and alert messages generated in the security devices; however, it takes a lot of time to inspect and analyze them because the security devices generate an overwhelming amount of logs and security events. In this paper, we propose SQC, an efficient method for analyzing network security through visualization. The proposed method monitors anomalies occurring in an entire network and displays detailed information of the attacks. In addition, by providing a detailed analysis of network attacks, this method can more precisely detect and distinguish them from normal events.

• Biomaterials and Biomechanics in Bioengineering
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• 제2권3호
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• pp.173-183
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• 2015

In obstetrics, cardiotocography is a procedure to record the fetal heartbeat and the uterine contractions usually during the last trimester of pregnancy. It helps to monitor patterns associated with the fetal activity and to detect the pathologies. In this paper, random forest classifier is used to classify normal, suspicious and pathological patterns based on the features extracted from the cardiotocograms. The results showed that random forest classifier can detect these classes successfully with overall classification accuracy of 93.6%. Moreover, important features are identified to reduce the feature space. It is found that using seven important features, similar classification accuracy can be achieved by random forest classifier (93.3%).

• Journal of Information Processing Systems
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• 제17권4호
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• pp.772-786
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• 2021

The process of tracking suspicious behavior manually on a system and gathering evidence are labor-intensive, variable, and experience-dependent. The system logs are the most important sources for evidences in this process. However, in the Microsoft Windows operating system, the action events are irregular and the log structure is difficult to audit. In this paper, we propose a model that overcomes these problems and efficiently analyzes Microsoft Windows logs. The proposed model extracts lists of both common and key events from the Microsoft Windows logs to determine detailed actions. In addition, we show an approach based on the proposed model applied to track illegal file access. The proposed approach employs three-step tracking templates using Elastic Stack as well as key-event, common-event lists and identify event lists, which enables visualization of the data for analysis. Using the three-step model, analysts can adjust the depth of their analysis.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• 한국조사연구학회지:조사연구
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• 제6권1호
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• pp.83-98
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• 2005

대규모 통계조사에서 수집된 데이터에는 오류나 결측값의 문제가 발생하기 마련이다. 조사, 데이터 입력, 데이터 처리 등의 과정에서 여러 가지 요인에 의해 이런 문제가 생길 수 있는데 이런 데이터를 방치한 채 통계를 생산할 경우 편향이나 다양한 분석에서의 불일치의 문제가 발생하게 되어 통계의 품질과 신뢰성을 떨어뜨릴 수 있으므로 수집된 데이터의 오류나 결측값을 찾아 수정하는 데이터편집은 매우 중요한 작업이다. 해외에서는 데이터편집의 문제를 공론화하여 다루고 있는 데 반해 우리나라에서 데이터편집에 관한 논의는 거의 없는 편이다. 본 연구의 목적은 주택가 격동향조사를 위한 데이터편집의 사례를 소개함으로 데이터편집에 대한 논의의 폭을 넓히는 데 있다. 조사목적에 맞도록 편집규칙을 정하는 과정 및 관련 자료들을 소개하고, 온라인조사라는 조사방식에 맞는 입력 데이터편집방법을 마련하여 실시하는 예들을 소개하며, 마지막으로 출력 데이터편집에 의해 입력 편집에서 걸러지지 않은 오류나 문제들을 제거하는 방법도 소개한다.