• Land and Housing Review
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• v.13 no.3
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• pp.107-113
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• 2022

With increasing airtight building construction aimed at reducing energy consumption, indoor relative humidity is increasing which can lead to condensation and moisture damage in multi-family residential buildings. This has led to increased implementation of mechanical ventilation to control indoor moisture. However mechanical ventilation systems consume additional energy and generate noise. As this leads to occupant discomfort, it is necessary to select a ventilation system that addresses the energy and noise issues. This research measured the ventilation performance, energy consumption, and noise level of mechanical ventilation devices in multi-family residential buildings. TOPSIS, a multi-criteria decision making technique was used to determine appropriate ventilation strategies in addition to occupant ventilation system operation preference.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.9-21
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• 2023

The recent conflict between Russia and Ukraine underscores the significant of military logistics support in modern warfare. Military logistics support is intricate and specialized, and traditionally centered on the mission-level operational analysis and functional models. Nevertheless, there is currently increasing demand for military logistics support even at the engagement level, especially for resupply using unmanned transport assets. In response to the demand, this study proposes a task model of the military logistics support for engagement-level analysis that relies on the logic of ammunition resupply below the battalion level. The model employs a decisions tree to establish the priority of resupply based on variables such as the enemy's level of threat and the remaining ammunition of the supported unit. The model's feasibility is demonstrated through a combat simulation using OneSAF.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.609-619
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• 2023

Recently, crimes using drones, one of the IoT industries have been continuously reported. In particular, drones are characterized by easy access and free movement, so they are used for various crimes such as transporting explosives, transporting drugs, and illegal recording. In order to analyze and investigate these criminal acts, drone forensic research is highly emphasized. Media data, PII, and flight records are digital forensic artifacts that can be acquired from drones, in particluar flight records are important artifacts since they can be used to trace drone activities. Therefore, in this paper, the characteristics of the deleted flight record files of DJI drones are presented and verified using the Phantom3, Phantom4 andMini2 models, two drones with differences in characteristics. Additionally, the recovery level is analyzed using the flight record file characteristics, and lastly, drones with the capacity to recover flight records for each drone model and drone models without it are classified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.449-451
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• 2021

Cyber-attacks targeting endpoints have developed sophisticatedly into targeted and intelligent attacks, Advanced Persistent Threat (APT) targeting the Industrial Internet of Things (IIoT) has increased accordingly. Machine learning-based Endpoint Detection and Response (EDR) solutions combine and complement rule-based conventional security tools to effectively defend against APT attacks are gaining attention. However, universal EDR solutions have a high false positive rate, and needs high-level analysts to monitor and analyze a tremendous amount of alerts. Therefore, the process of optimizing machine learning-based EDR solutions that consider the characteristics and vulnerabilities of IIoT environment is essential. In this study, we analyze the flow and impact of IIoT targeted APT cases and compare the method of machine learning-based APT detection EDR solutions.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.579-585
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• 2023

The purpose of this study is to explore keywords within stalking-related news articles according to political orientation through the text network analysis, and then to examine the implicit intentions. Selecting total 1,607 articles including 824 articles of the conservative press(The Chosun Ilbo, The Joongang Ilbo) and 783 articles of the progressive press(The Hankyoreh, The Kyunghyang Shinmun) reported from January 1, 2018 to December 31, 2022, this study explored the aspect of topic category drawn through the topic modeling technique based on LDA(Latent Dirichlet Allocation). In the results of this study, the common topics of the conservative and progressive press were improvement of the perception of gender-based violence, personal protection & intensity of punishment, and disclosure of stalkers' personal information. Regarding the topics differently shown in those two press, the conservative press showed stalkers' harmful act, and outline of 'murder case at Sindang Station' while the progressive press showed request for aggravated punishment on the 'murder case at Sindang Station', and eradication of sexual exploitation crime (in cyber space). The results of this study imply that there are changes in the type of reporting according to ideological opinions about stalking in news articles.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1021-1031
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• 2023

State-sponsored cyber attacks are highly impactful because they are carried out to achieve pre-planned goals. As a defender, it is difficult to respond to them because of the large scale of the attack and the possibility that unknown vulnerabilities may be exploited. In addition, overreacting can reduce the availability of users and cause business disruption. Therefore, there is a need for a response policy that can effectively defend against attacks while ensuring user availability. To solve this problem, this paper proposes a method to collect the number of processes and sessions of defense assets in real time and use them for learning. Using this method to learn reinforcement learning-based policies on a cyber attack simulator, the attack duration based on 100 time-steps was reduced by 27.9 time-steps and 3.1 time-steps for two attacker models, respectively, and the number of "restore" actions that impede user availability during the defense process was also reduced, resulting in an overall better policy.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.99-105
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• 2023

In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.

• Journal of Wetlands Research
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• v.26 no.1
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• pp.114-125
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• 2024

Wetlands in the living area are important ecological resources that are the basis for the daily life or farming activities of local residents, and have high ecological value such as ecosystem services and green infrastructure. This study was carried out to develop a functional evaluation methodology optimized for small-scale wetlands(RAMS). Based on on-site evaluation by experts, surveys and in-depth interviews, four functional items, including biodiversity, health, hydrophilic culture and ecology, water circulation, and carbon absorption, and 15 detailed indicators, and the evaluation grade for each detailed indicator, were developed on a 5-point scale. The evaluation methodology optimized for small-scale living areas wetlands (RAMS) proposed as a result of this study can be used as basic data for conservation and restoration and management of small-scale living areas wetlands at home and abroad.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.451-461
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• 2024

Recently, various AI chatbots based on large language models have been released. Chatbots have the advantage of providing users with quick and easy information through interactive prompts, making them useful in various fields such as question answering, writing, and programming. However, a vulnerability in chatbots called "prompt injection attacks" has been proposed. This attack involves injecting instructions into the chatbot to violate predefined guidelines. Such attacks can be critical as they may lead to the leakage of confidential information within large language models or trigger other malicious activities. However, the vulnerability of Korean prompts has not been adequately validated. Therefore, in this paper, we aim to generate malicious Korean prompts and perform attacks on the popular chatbot to analyze their feasibility. To achieve this, we propose a system that automatically generates malicious Korean prompts by analyzing existing prompt injection attacks. Specifically, we focus on generating malicious prompts that induce harmful expressions from large language models and validate their effectiveness in practice.

• Korean journal of communication and information
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• v.67
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• pp.209-240
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• 2014

This study observes reporters' awareness on the use of social media and their Core Values by using the Theory of Means-End Chain and drawing conclusions from a Hierarchical Value Map (HVM). In order to analyze reporters' knowledge and awareness on the use of social media via the laddering method, in-depth interviews of 46 reporters were conducted. The study showed that reporters consider sense of kinship, well-balanced understanding, and the desire for knowledge to be important Core Values. The results revealed that the convenience in interpersonal communication, development of intimacy in relationships, entertainment and affection, curiosity, the reduction in the cost of the acquisition of information, understanding of trends in issues, a peek into new information and the maintenance of interests, psychological dependability, and quick updates on information items are considered important Consequences of social media. In the Attributes level, the ability to write postings and links on Facebook and readability and 'follow' and 'mention' features on Twitter were confirmed to be important items in social media. The findings infer that reporters that make use of social media use Twitter and Facebook to build a sense of kinship with other users and gain well-balanced understanding by accessing a lot of information through social media. While this study examined the level of reporters' familiarity with the use of social media via the laddering method, the results cannot be seen as a generalization, as the interviewees were reporters from only the major news organizations.