• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.3
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• pp.91-101
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• 2005

Although the studies on the analysis and classification of source-level vulnerabilities in operating systems are not direct and positive solutions to the exploits with which the host systems are attacked, It is important in that those studies can give elementary technologies in the development of security mechanisms. But, whereas Linux systems are widely used in Internet and intra-net environments recently, the information on the basic and fundamental vulnerabilities inherent in Linux systems has not been studied enough. In this paper, we propose characteristic classification and correlational analyses on the source-level vulnerabilities in Linux kernel that are opened to the public and listed in the SecurityFocus site for 6 years from 1999 to 2004. This study may contribute to expect the types of attacks, analyze the characteristics of the attacks abusing vulnerabilities, and verify the modules of the kernel that have critical vulnerabilities.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.225-238
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• 2021

The ROC, TOC, and TROC curves, which are visually descriptive methods of exploring the performance of the binary classification model, are implemented with TP, TN, FP, FN which consist of the confusion matrix, as well as their ratios TPR, TNR, FPR, FNR. In this study, we consider two types odds and then propose an odds curve representing these odds. And show the relationship between the odds curve and ROC curve. Based on the odds curve, we propose not only two statistics that measure the discriminant power of the odds curve but also the criteria for validation ratings of the odds curve. According to the shape of the odds curves, two classification distributions can be estimated and a criterion for validation ratings can be determined. The odds curve can be meaningfully used like other visual methods, and two kinds of measures for the discriminant power can be also applied together as an alternative criterion.

• Journal of the Korean Institute of Gas
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• v.27 no.1
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• pp.48-56
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• 2023

Currently, Hydrogen compressor is maintained and managed according to the safety management regulations of the operator. But it is not based on technical standards, so it is necessary to establish based on reliability. In this paper, hydrogen compressor taxonomy by ISO 14224 standard reviewed for hydrogen compressor operated by KOGAS-Tech hydrogen vehicle refueling station to establish 9-stage taxonomy, and FMEA was conducted to establish RCM strategy specified in SAE JA1011, and 1012. It is expected that results of taxonomy and RCM strategy will be used as basic data for development of standards for verifying the performance of compressors.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.359-366
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• 2021

A number of construction companies in Korea invest considerable human and financial resources to construct a system for managing apartment defect data and for categorizing repair tasks. Thus, this study proposes machine learning models to automatically classify defect complaint text-data into one of the sub categories of 'finishing work' (i.e., one of the defect repair tasks). In the proposed models, we employed two word representation methods (Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF)) and two machine learning classifiers (Support Vector Machine, Random Forest). In particular, we conducted both binary- and multi- classification tasks to classify 9 sub categories of finishing work: home appliance installation work, paperwork, painting work, plastering work, interior masonry work, plaster finishing work, indoor furniture installation work, kitchen facility installation work, and tiling work. The machine learning classifiers using the TF-IDF representation method and Random Forest classification achieved more than 90% accuracy, precision, recall, and F1 score. We shed light on the possibility of constructing automated defect classification systems based on the proposed machine learning models.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.41-49
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• 2021

All application programs, including malware, call the Application Programming Interface (API) upon execution. Recently, using those characteristics, attempts to detect and classify malware based on API Call information have been actively studied. However, datasets containing API Call information require a large amount of computational cost and processing time. In addition, information that does not significantly affect the classification of malware may affect the classification accuracy of the learning model. Therefore, in this paper, we propose a method of extracting a essential feature set after reducing the dimensionality of API Call information by applying various feature selection methods. We used CICAndMal2020, a recently announced Android malware dataset, for the experiment. After extracting the essential feature set through various feature selection methods, Android malware classification was conducted using CNN (Convolutional Neural Network) and the results were analyzed. The results showed that the selected feature set or weight priority varies according to the feature selection methods. And, in the case of binary classification, malware was classified with 97% accuracy even if the feature set was reduced to 15% of the total size. In the case of multiclass classification, an average accuracy of 83% was achieved while reducing the feature set to 8% of the total size.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.276-284
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• 2020

This paper proposes a new convolutional neural network (CNN) based spectrum sensing technique for cognitive radio communications. The proposed technique determines the existence of the primary user (PU) by using energy detection without any prior knowledge of the PU's signal. In the proposed method, the received signal is high-rate sampled to sense the entire spectrum bands of interest. After that, fast Fourier transform (FFT) of the signal converts the time domain signal to frequency domain spectrum and by stacking those consecutive spectrums, a 2 dimensional signal is made. The 2 dimensional signal is cut by the sensing channel bandwidth and inputted to the CNN. The CNN determines the existence of the primary user. Since there are only two states (existence or non-existence), binary classification CNN is used. The performance of the proposed method is examined through computer simulation and indoor experiment. According to the results, the proposed method outperforms the conventional threshold-based method by over 2 dB.

• Korean Journal of Plant Resources
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• v.35 no.2
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• pp.201-241
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• 2022

This study was investigated distributed the vascular flora of around Haemyeong Mt., located in Seokmodo, Incheon, and compared invasive alien plants in the surrounding forest 14 areas. This study carried out to be established a monitoring system for the remarkable plants etc. and used as basic data for biodiversity enhancement and conservation. The survey was conducted 19 times from April 2019 to October 2020. A total of 107 families, 382 genera, 616 species, 15 sub-species, 55 varieties, 8 formas, and 694 taxa were classified in the flora around Haemyeong Mt. areas. 17 taxa for Korea endemic plants. 12 taxa were classified for rare plants, and a total of 79 taxa were identified for floristic target species I~V. Halophytes consisted of 37 taxa. The invasive alien plants were classified as 66 taxa, and also there has been 126 taxa of them in Seokmodo and surrounding 14 forests. Plants that appeared in all 14 areas out of 126 taxa were classified as 7 taxa, such as Erigeron annuus. However, 48 taxa appeared only once in some areas, but it seems inevitable that they will spread in the future. Therefore, it is necessary to prepare a long-term conservation plan for native plants.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.10
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• pp.38-45
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• 2004

In this paper, a parallel visualization algorithm is proposed for efficient visualization of the massive data generated from large-scale parallel finite element analysis through investigating the characteristics of parallel rendering methods. The proposed parallel visualization algorithm is designed to be highly compatible with the characteristics of domain-wise computation in parallel finite element analysis by using the sort-last-sparse approach. In the proposed algorithm, the binary tree communication pattern is utilized to reduce the network communication time in image composition routine. Several benchmarking tests are carried out by using the developed in-house software, and the performance of the proposed algorithm is investigated.

• 한국생물공학회:학술대회논문집
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• 2003.04a
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• pp.715-718
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• 2003

A COG (clusters of orthologous groups of proteins) algorithm, protein similarities among genomes, was used to detect conserved genes and to figure out their relationships within 42 procaryote, 33 Bacteria and 16 Proteobacteria All analyzed procaryotes shared 75 COGs. COG0195, COG0358 and COG0528 were only represented by the 42 procaryotes. Sixty-four COGs were added as conserved genes in 33 eubacteria. Each Proteobacteria group has a unique repertoire of COGs. Metabolic COGs were more diverse in the beta-Proteobacteria group than in the other groups. The possibilities of detecting new biological molecules is high in phylogenetically related organisms, hence the identification of useful proteins by using this algorithm is possible.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.33-41
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• 2006

This paper presents a method that automatically recognizes products into model type, which it flows with the conveyor belt. The specific interlaced image are occurred by moving image when we use the NTSC based camera. It is impossible to process interlaced images, so a suitable post-processing is required. For the purpose of this processing, after it remove interlaced images using de-interlacing method, it leads rectangle region of object by thresholding. And then, after rectangle region is separated into several blocks through edge detection, we calculate pixel numbers per each block, re-classify using its average, and classify products into model type. Through experiments, we know that the proposed method represent high classification ratio.