• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.21-31
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• 2018

Cyber penetration attacks can not only damage cyber space but can attack entire infrastructure such as electricity, gas, water, and nuclear power, which can cause enormous damage to the lives of the people. Also, cyber space has already been defined as the fifth battlefield, and strategic responses are very important. Most of recent cyber attacks are caused by malicious code, and since the number is more than 1.6 million per day, automated analysis technology to cope with a large amount of malicious code is very important. However, it is difficult to deal with malicious code encryption, obfuscation and packing, and the dynamic analysis technique is not limited to the performance requirements of dynamic analysis but also to the virtual There is a limit in coping with environment avoiding technology. In this paper, we propose a machine learning based malicious code analysis technique which improve the weakness of the detection performance of existing analysis technology while maintaining the light and high-speed analysis performance applicable to commercial endpoints. The results of this study show that 99.13% accuracy, 99.26% precision and 99.09% recall analysis performance of 71,000 normal file and malicious code in commercial environment and analysis time in PC environment can be analyzed more than 5 per second, and it can be operated independently in the endpoint environment and it is considered that it works in complementary form in operation in conjunction with existing antivirus technology and static and dynamic analysis technology. It is also expected to be used as a core element of EDR technology and malware variant analysis.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

• ETRI Journal
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• v.44 no.4
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• pp.573-587
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• 2022

The agricultural sector is completely different from other sectors since it completely relies on various natural and climatic factors. Climate changes have many effects, including lack of annual rainfall and pests, heat waves, changes in sea level, and global ozone/atmospheric CO₂ fluctuation, on land and agriculture in similar ways. Climate change also affects the environment. Based on these factors, farmers chose their crops to increase productivity in their fields. Many existing agricultural ontologies are either domain-specific or have been created with minimal vocabulary and no proper evaluation framework has been implemented. A new agricultural ontology focused on subdomains is designed to assist farmers using Jaccard relative extractor (JRE) and Naïve Bayes algorithm. The JRE is used to find the similarity between two sentences and words in the agricultural documents and the relationship between two terms is identified via the Naïve Bayes algorithm. In the proposed method, the preprocessing of data is carried out through natural language processing techniques and the tags whose dimensions are reduced are subjected to rule-based formal concept analysis and mapping. The subdomain ontologies of weather, pest, and soil are built separately, and the overall agricultural ontology are built around them. The gold standard for the lexical layer is used to evaluate the proposed technique, and its performance is analyzed by comparing it with different state-of-the-art systems. Precision, recall, F-measure, Matthews correlation coefficient, receiver operating characteristic curve area, and precision-recall curve area are the performance metrics used to analyze the performance. The proposed methodology gives a precision score of 94.40% when compared with the decision tree(83.94%) and K-nearest neighbor algorithm(86.89%) for agricultural ontology construction.

• Information Systems Review
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• v.25 no.1
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• pp.1-26
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• 2023

Consumers tend to trust information provided by other consumers more than information provided by sellers. Therefore, while inducing consumers to write high-quality reviews is a very important task for companies, it is not easy to produce such high-quality reviews. Based on previous research on review writing and memory recall, we decided to develop a way to use digital nudge to help consumers naturally write high-quality reviews. Specifically, we designed an experiment to verify the effect of uploading a photo during the online review process on the quality of review of the review writer. We then recruited subjects and then divided them into groups that upload photos first and groups that do not. A task was assigned to each subject to write positive and negative reviews. As a result, it was confirmed that the behavior of uploading a photo first increases the review length. In addition, it was confirmed that when online users who upload photos first have extremely negative satisfaction with the product, the extent of two-sidedness of the review content increases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.446-465
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• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.48-52
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• 2007

As huge amount of literature including biological data is being generated after post genome era, it becomes difficult for researcher to find useful knowledge from the biological databases. Bio-text mining and related natural language processing technique are the key issues in the intelligent knowledge retrieval from the biological databases. We propose a bio-text mining technique for the biologists who find Knowledge from the huge literature. At first, web robot is used to extract and transform related literature from remote databases. To improve retrieval speed, we generate an inverted file for keywords in the literature. Then, text mining system is used for extracting given knowledge patterns and keywords. Finally, we construct a grid computing environment to guarantee processing speed in the text mining even for huge literature databases. In the real experiment for 10,000 bio-literatures, the system shows 95% precision and 98% recall.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.688-701
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• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1006-1013
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• 1988

We have shown how unstructured modeling was used to derive a general stability condition in Part 1. In Part 2, we focus on the particular dynamics (structures modeling) of the robot manipulator and environment. Using rigid body dynamics, the stability condition for the direct drive robots has been achieved in terms of the Jacobian and robot tracking controller. Combining the structured and unstructured modeling, a stability condition for a particular application can be obtained. This approach has been used to analyze compliant motion on the University of Minnesota robot using a feedforward torque controller. We have obtained a stability condition for this application. Through both simulation and experiment, the sufficiency of this condition has been demonstrated. For a sufficient stability condition, recall that if the condition is satisfied, then the stability is guaranteed; however, if the condition is violated, no conclusion can be made.

• Journal of the Korean Chemical Society
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• v.62 no.2
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• pp.159-165
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• 2018

For the first time, the spot the differences (STD) game was employed in the teaching of basic organic chemistry course. Three sets of paired pictures associated with selected topics in organic chemistry were presented to the students and they were required to spot the differences between the two pictures. Based on the students' pre and post self-assessment, the STD game resulted in several positive learning outcomes as indicated in the students' reflective writing, including knowledge recall, deeper understanding of a subject, enhanced analytical skill, motivation and fun-filled learning, learning from peers and self-empowerment in learning. The STD game is a desirable teaching and learning tool, as learning in an entertaining and interactive way is highly sought after in today's classroom, especially to novice students. In the future, the STD game can be modified and implemented to cater the needs of different courses and topics.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.11
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• pp.950-957
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• 1989

We have shown how unstructured modeling was used to derive a general stability condition in Part 1. In Part 2, we focus on the particular dynamics (structured modiling) of the robot manipulator and environment. Using rigid body dynamics, the stability condition for the direct drive robots has been achieved in terms of the Jacobian and robot tracking controller. Combining the structured and unstructured modeling, a stability condition for a particular application can be obtained. This approach has been used to analyze compliant motion on the University of Minnesota robot using a feedforward torque controller. We have obtained a stability condition for this application. Through both simulation and experiment, the sufficiency of this condition has been demonstrated. For a sufficient stability condition, recall that if the condition is satified, then the stability is guaranteed` however, if the condition is violated, no conclusion can be made.