• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.49-62
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• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.356-366
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• 2021

The high-tech industry, a leader in the national economy, has a larger investment cost compared to general buildings, a shorter construction period, and requires continuous investment. Therefore, accurate construction cost prediction and quick decision-making are important factors for efficient cost and process management. Overseas, the construction information classification system has been standardized since 1980 and has been continuously developed, improving construction productivity by systematically collecting and utilizing project life cycle information. At domestic construction sites, attempts have been made to standardize the classification system of construction information, but it is difficult to achieve continuous standardization and systematization due to the absence of a standardization body and differences in cost and process management methods for each construction company. Particular, in the case of the high-tech industry, the standardization and systematization level of the construction information classification system for high-tech facility construction is very low due to problems such as large scale, numerous types of work, complex construction and security. Therefore, the purpose of this study is to construct a construction information classification system suitable for high-tech facility construction through collection, classification, and analysis of related project data constructed in Korea. Based on the WBS (Work Breakdown Structure) and CBS (Cost Breakdown Structure) classified and analyzed through this study, a code system through hierarchical classification was proposed, and the cost model of buildings by linking WBS and CBS was three-dimensionalized and the utilized method was presented. Through this, an information classification system based on inter-relationships can be developed beyond the one-way tree structure, which is a general construction information classification system, and effects such as shortening of construction period and cost reduction will be maximized.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3230-3253
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• 2017

Android malware steals users' private information, and embedded unsafe advertisement (ad) libraries, which execute unsafe code causing damage to users. The majority of such traffic is HTTP and is mixed with other normal traffic, which makes the detection of malware and unsafe ad libraries a challenging problem. To address this problem, this work describes a novel HTTP traffic flow mining approach to detect and categorize Android malware and unsafe ad library. This work designed AndroCollector, which can automatically execute the Android application (app) and collect the network traffic traces. From these traces, this work extracts HTTP traffic features along three important dimensions: quantitative, timing, and semantic and use these features for characterizing malware and unsafe ad libraries. Based on these HTTP traffic features, this work describes a supervised classification scheme for detecting malware and unsafe ad libraries. In addition, to help network operators, this work describes a fine-grained categorization method by generating fingerprints from HTTP request methods for each malware family and unsafe ad libraries. This work evaluated the scheme using HTTP traffic traces collected from 10778 Android apps. The experimental results show that the scheme can detect malware with 97% accuracy and unsafe ad libraries with 95% accuracy when tested on the popular third-party Android markets.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.21-29
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• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.

• Journal of Environmental Impact Assessment
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• v.7 no.2
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• pp.103-112
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• 1998

Nature-friendly river project has became common practice in Japan. In order to make it available for the conservation and rehabilitation of desirable water environment, water criteria for water environmental assessment must be established. Especially, the criteria estimating the effects on ecosystem in and around river should be constructed. In this paper, classification method for water quality has been developed using biological indices and applied to observed data in Honmyo River, Nagasaki, Japan. Modified PI method (BI') has been suggested and those of three most abundant species resulted effective estimate for an overall water quality with comparatively simple procedure. Extensive database management code was prepared for the comprehensive ecological monitoring of river basin, which includes various biota. That system enables easy access of all the ecological data for a dissemination of a sound and sustainable water environment. The result of this study could improve knowledge base, serve making consensus for citizens, and help river management plans. In Japan, citizen's realization and action are the most critical factor for nature-friendly river restoration project.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.2
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• pp.167-174
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• 2002

Earth retaining structure is constructed structure in order to construct a multistoried building, the subway, a subterranean downtown for effective use and obtainments of the limited ground. Recently, many kinds of research have been actively developed for a standardization and a database on designing and constructing of bridge, tunnel, road. With the works of database construction of that, many kinds of data with respect to statistics is cumulated. However, Database work of designed and constructed earth retaining structure in the construction field is wholly lacking and lagged behind in the works of database construction. This paper suggested classification system on indication data in connection with designing and constructing earth retaining structures a hundred fields. On the basis of that, code work with classification system was practised and DB program of indication data in connection with designing and constructing earth retaining structures was developed.

• Journal of the Korean Society for Railway
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• v.19 no.5
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• pp.672-684
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• 2016

UIC Codes 714R & 715R recommend the use of line classifications and their usage in maintenance work by employing notional traffic loads. However, the classification has not been applied to local lines and, therefore, a new line classification system based on UIC 714R has been proposed in this study. For this, various classification models of UIC, Germany, and UK have been studied first and equivalent traffic loads based on Korail's report, as well as on train timetables, have been derived. The results of the classifications have been compared with those of major European countries and it has been shown that the proposed classification is equivalent to the average value in the European cases. The line classification can be fully utilized during the decision making process of maintenance work and will also be used to model the Reliability Centered Maintenance (RCM) in the future.

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.87-93
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• 2019

Revision work on the Codex Classification of Foods and Animal Feeds was undertaken in 2007 and presently, revisions for most food groups have been completed. For vegetables, the work was conducted during 2014-2017, and the final draft revision was adopted by the $40^{th}$ Codex Alimentarius Commission (2017). Here, the revised classification of vegetable commodities is introduced in order to be utilized in various food-related fields, in particular, food safety regulation. The revised classification is briefly summarized as follows: Codex classified vegetables into 10 groups (Group 009-018): bulb vegetables (Group 009), Brassica vegetables (except Brassica leafy vegetables) (Group 010), fruiting vegetables, Cucurbits (Group 011), fruiting vegetables, other than Cucurbits (Group 012), leafy vegetables (including Brassica leafy vegetables) (Group 013), legume vegetables (Group 014), pulses (Group 015), root and tuber vegetables (Group 016), stalk and stem vegetables (Group 017) and edible fungi (Group 018). The groups are further divided into a total of 33 subgroups. In the Classification, 430 different commodity codes are assigned to vegetable commodities. Meanwhile, Korea's Ministry of Food and Drug Safety (MFDS) does not include potatoes, beans and mushrooms within a vegetable group. In addition, the MFDS divides one vegetable group into six subgroups including flowerhead Brassicas, leafy vegetables, stalk and stem vegetables, root and tuber vegetables, fruiting vegetables, Cucurbits, and fruiting vegetables other than Cucurbits. Therefore, care is needed in using the Codex Classification.

• The Journal of Internal Korean Medicine
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• v.31 no.1
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• pp.1-10
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• 2010

Objectives : It is necessary that the international classification of diseases (ICD) be examined in order to comprise the third revision of the Korean Classification of Disease on Oriental Medicine (KCD-OM) and disease classification in the oriental internal medicine field. It is essential that the selection, classification and definition of disease and pattern names of oriental concepts in internal medicine be clear. Since 2008, the fifth revision of the Korean Classification of Disease (KCD-5) has been used in Korea. It was required to use the reference classification from the Oriental medicine area based on the ICD-10. Methods : In this review, the necessity for, meaning of and content of the third revision are briefly described. The ICD system was reviewed and KCD-OM was reconstructed. How diagnosis in the oriental internal medicine area had changed is discussed. Review and Results : In 1973, the disease classification of oriental medicine was established the basis on the contents of Dongeuibogam. It was irrespective of the ICD. As to the classification system in the Oriental internal medicine field, systemic disease was comprised of wind, cold, warm, wet, dryness, heat, spirit, ki, blood, phlegm and retained fluid, consumptive disease, etc. Diseases of internal medicine comprised a system according to the five viscera and the six internal organs and followed the classification system of Dongeuibogam. The first and second revisions were of the classification system based on the curriculum in 1979 and 1995. In 1979, in the first revision, geriatric disease and idiopathic types of disease were deleted, and skin disease was included among surgery diseases. This classification was expanded to 792 small classification items and 1,535 detailed classification items to the dozen disease classes. In 1995, in the second revision, it was adjusted to 644 small classes and 1,784 detailed classification items in the dozen disease classes. KCD-OM3 did KCD from this basis. It added and comprised the oriental medical doctor's concept names of diseases considering the special conditions in Korea. KCD-OM3 examined the KCD-OMsecond revised edition (1994). It improved the duplex classification, improper classifications, etc. It is difficult for us to separate the disease names and pattern names in oriental medicine. We added to the U code and made one classification system. By considering the special conditions in Korea, 169 codes (83 disease name codes, 86 pattern name codes) became the pre-existence classification and links among 306 U codes of KCD-OM3. 137 codes were newly added in the third revision. U code added 3 domains. These are composed of the disease name (U20-U33, 97 codes), the disease pattern name (U50-U79, 191 codes) and the constitution pattern name of each disease (U95-U98, 18 codes). Conclusion : The introduction of KCD-OM3 conforms to the diagnostic system by which oriental medical doctors examine classes used with the basic structure of the reference classification of WHO and raises the clinical study and academic activity of the Korean oriental medicine and makes the production of all kinds of nation statistical indices possible. The introduction of KCD-OM3 promotes the diagnostic system by which doctors of Oriental medicine examine classes using the association with KCD-5. It will raise the smoothness and efficiency of oriental medical treatment payments in the health insurance, automobile insurance, industrial accident compensation insurance, etc. In addition, internationally, the eleventh revision work of the ICD has been initiated. It needs to consider incorporating into the International Classification of Diseases some of every country's traditional medicine.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1881-1892
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• 1997

This paper proposes the method for recognition of printed chinese characters by probabilistic VQ networks and multi-stage recognizer has hierarchical structure. We use modular neural networks, because it is difficult to construct a large-scale neural network. Problems in this procedure are replaced by probabilistic neural network model. And, Confused Characters which have significant ratio of miss-classification are reclassified using the entropy theory. The experimental object consists of 4,619 chinese characters within the KSC5601 code except the same shape but different code. We have 99.33% recognition rate to the training data, and 92.83% to the test data. And, the recognition speed of system is 4-5 characters per second. Then, these results demonstrate the usefulness of our work.