• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.93-103
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• 2021

Despite the efforts of financial authorities in conducting the direct management and supervision of collection agents and bond-collecting guideline, the illegal and unfair collection of debts still exist. To effectively prevent such illegal and unfair debt collection activities, we need a method for strengthening the monitoring of illegal collection activities even with little manpower using technologies such as unstructured data machine learning. In this study, we propose a classification model for illegal debt collection that combine machine learning such as Support Vector Machine (SVM) with a rule-based technique that obtains the collection transcript of loan companies and converts them into text data to identify illegal activities. Moreover, the study also compares how accurate identification was made in accordance with the machine learning algorithm. The study shows that a case of using the combination of the rule-based illegal rules and machine learning for classification has higher accuracy than the classification model of the previous study that applied only machine learning. This study is the first attempt to classify illegalities by combining rule-based illegal detection rules with machine learning. If further research will be conducted to improve the model's completeness, it will greatly contribute in preventing consumer damage from illegal debt collection activities.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.4
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• pp.769-788
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• 2014

Today, all the countries of the world newly recognize importance of sea on 70% area of the earth, which are focused on efforts for security of marine territory and fishes resources. On the security concerns of the ocean & fishes resources, Sea are very important on the ground of the importance of the ocean, thus international community has been trying to combat a maritime security threat and illegal fisheries. Coastal states need to have proper state's jurisdiction and exercise it's jurisdiction to response effectively to a maritime security threat and illegal fisheries. Here, many of the coastal states strengthened the rights in Exclusive Economic Zone(;EEZ) naturally, there are made cooperation activities and keen competition in the sea because deepening of complex understanding of the relationship between the surrounding countries with marine surveys & continental shelf development, island territorial sovereignty & marine jurisdiction in overlap of sea area on EEZ. In these circumstances, foreign fishing boats invaded to our territorial waters and EEZ many times. in addition, Chinese fishing boats are going to illegal fisheries naturally. On this point, a powerful crackdown of maritime enforcement organization had no effect on them. Also more and more their resistance gathered strength and tendency of a illegal activities became systematization, group action and atrocity little by little. So this thesis includes a study on the regal regulation, the system and formalities on the control of illegal fishing. And the author analyzed the details of the activities of illegal fishing and boats controlled by Korea Coast Guard(KCG), fishing patrol vessels of Ministry of Maritime Affaires and Fisheries(MOMAF) and Navy etc. from in adjacent sea area of Korea. In relation to this, the policy and activity plan were devised to crackdown to illegal fisheries of foreign fishing boats and then it was enforced every year. According to this, analyze the present conditions of illegal fisheries of a foreign fishing boats on this study, also analyze the present conditions of maritime enforcement organization & found out problems to compared it. protect the territorial waters, at the same time protection of marine mineral resources & fishes resources of EEZ including continental shelf, which has want to study for the role & response of maritime enforcement organization for the protection of fisheries resources and a proper, a realistic confrontation plan of maritime enforcement organization against illegal fisheries of foreign fishing boats.

• Fashion & Textile Research Journal
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• v.10 no.6
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• pp.907-916
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• 2008

The purposes of this study were to examine the consumer beliefs on the wrongness and occurrence of unethical behaviors in retail settings, to examine the differences in unethical beliefs by consumer characteristics, and to examine the effects of anomie on unethical beliefs. A total of 609 questionnaires collected from a consumer survey were analyzed. Results revealed that respondents tended to perceive illegal activities as the most unethical and the least prevalent behaviors and downloading intellectual properties as the least unethical and the most prevalent behaviors. There were differences by age, marital status, occupation, and education in the four dimensions of unethical beliefs including actively benefiting from illegal/deceiving activities, passively benefiting at the expense of the seller, no harm/no foul, and common but questionable actions. Partial differences were observed by shopping frequency and return experience. Valuelessness of anomie affected actively benefiting from illegal/deceiving activities and no harm/no foul.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.105-110
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• 2017

It is natural that the medical advertisement should be guaranteed as part of the basic commercial activities of medical institutions. However, the general public are non-specialist and they have less informed about medical care than medical specialists, and it is not easy to judge and select medical information. Also, if someone damaged by illegal medical advertising, it cannot be recovered to the original. In this regard, medical advertising has been legislated so that medical organizations can pre - screen the medical laws. However, In December 2015, after the Constitutional Court ruled unconstitutional preliminary censorship, it became virtually impossible to pre-screen. In recent years, illegal medical advertisement have been on the rise, and false and exaggerated medical advertising are increasing the damage to medical consumers. Therefore it is urgent to take countermeasures about this. Thus, this paper try to analyzes the characteristics of general commercial and other medical advertisements and looks for alternatives that can minimize the damage caused by illegal medical advertisements and institutional weaknesses by analyzing the regulatory trends in medical advertising.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1097-1114
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• 2015

As the online game market grows, illegal activities such as cheating play using game bots or game hack programs, running private servers, hacking game companies' system and network, and account theft are also increasing. There are various security measures for online games to prevent illegal activities. However, the current security measures are not enough to prevent all highly evolving game attacks and frauds. Some security measure can do harm game players usability, game companies need to develop usable security measure that is well fit to game genre and contents design. In this study, we surveyed the recent trend of various security measure applied in online games. This research also classified illegal activities and their related countermeasure for detection and prevention.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.2
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• pp.53-62
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• 2024

This study assessed the efficiency of reducing soil erosion and suspended sediment through the restoration of alpine unauthorized and illegally cultivated fields, using the SWAT (Soil and Water Assessment Tool) model in the Mandae District. The results showed that in Scenario 5, which involved restoring unauthorized and illegal fields within forests, along rivers (banks), and in ditch areas were restored to their original land categories, achieved the highest efficiency in reducing average annual soil erosion and suspended sediment, with reductions of 8.1% and 4.5%, respectively. In particular, it was confirmed that the restoration of unauthorized and illegal fields within forested areas has a significant impact. This demonstrated that the restoration of unauthorized and illegal agricultural fields can substantially reduce the soil erosion and suspended sediment attributable to non-point source pollution. Our findings highlight the importance of managing these unauthorized and illegal agricultural activities in developing sustainable strategies within non-point source pollution management areas. This study is expected to provide important basic data to effectively establish water quality improvement strategies in the region of non-point source pollution management.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.275-279
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• 2022

The growth of technology nowadays has made many things easy for humans. These things are from everyday small task to more complex tasks. Such growth also comes with the illegal activities that are perform by using technology. These illegal activities can simple as displaying annoying message to big frauds. The easiest way for the attacker to perform such activities is to convenience user to click on the malicious link. It has been a great concern since a decay to classify URLs as malicious or benign. The blacklist has been used initially for that purpose and is it being used nowadays. It is efficient but has a drawback to update blacklist automatically. So, this method is replace by classification of URLs based on machine learning algorithms. In this paper we have use four machine learning classification algorithms to classify URLs as malicious or benign. These algorithms are support vector machine, random forest, n-nearest neighbor, and decision tree. The dataset that is used in this research has 36694 instances. A comparison of precision accuracy and recall values are shown for dataset with and without preprocessing.

• Journal of Fisheries and Marine Sciences Education
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• v.17 no.2
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• pp.170-179
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• 2005

Realities of illegal fisheries in the central and southern coastal areas of the Yellow Sea were investigated. The study was based on the data released by the Ministry of Maritime Affairs and Fisheries (MOMAF) and Korea Coast Guard (KCG) during 1992-2002 and on questionnaire responses. Analyses of KCG data showed that the number of enforcements by the agency gradually decreased during 1998-2001 but rose in 2002. Analyses of the MOMAF data, however, revealed that illegal fisheries gradually increased during 1992-1996, but sharply increased after 1997, and that such illegal activities became more common in the East Sea beginning in 2001. MOMAF data also showed that although illegal fisheries began to increase in the Yellow Sea after 1997 they tended to decrease in the southern sea after 1998, with a high rate of small-bull trawlers (40.9%) that were non-sanction fisheries (38.1%). Questionnaire responses showed that illegal fisheries were mainly motivated by poverty (27.4%) and largely occurred in coastal fisheries (78.0%). Analyses of questionnaire responses also suggested that illegal fishing activities can be reduced through tougher laws regulating fisheries.

• Journal of Distribution Science
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• v.20 no.7
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• pp.107-117
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• 2022

Purpose: The scope of forensic investigations serves to identify malicious activities, including leakage of crucial corporate information. The investigations also identify security lapses in available networks. The purpose of the present study is to explore how to block distribution channels to protect illegal leakage in supply chain through digital forensic method. Research design, data and methodology: The present study conducted the qualitative textual analysis and its data collection process entails five steps: identifying and collecting data, determining coding categories, coding the content, checking validity and reliability, and analyzing and presenting the results. This methodology is a significant research method due to its high quality of previous resources. Results: Applying previous literature analysis to the results of this study, the author figured out that there are four solutions as an evidences to block distribution channels, preventing illegal leakage regarding company information. The following subtitles show clear solutions: (1) Communicate with Stakeholders, (2) Preventing and addressing illegal leakage, (3) Victims of Data Breach, (4) Focusing Solely on Technical Teams. Conclusion: There are difficult scenarios that continue to introduce difficult questions surrounding engagement with digital evidence. Consequently, it is important to enhance data handling to provide answers for organizations that suffer due to illegal leakages of sensitive information.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.