• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.371-373
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• 2024

디지털 헬스케어 기술이 고도화되면서 디지털 치료제와 원격 의료 서비스가 의료 산업과 일상생활에 널리 활용되고 있다. 그러나 빅데이터 기반의 AI 서비스가 보편화될 수 있도록 데이터 수집, 가공, 활용 과정에서 개인정보가 남용되거나 유출되는 보안 위협도 증가하고 있다. 본 논문에서는 AI 챗봇을 활용한 정신건강 서비스를 위한 보안 위협 대응책을 마련하고 개인정보보호 가이드라인을 수립하여 사용자들에게 안전한 서비스를 제공하고 개인정보보호를 강화하는 AI 챗봇 서비스를 위한 데이터 보안 가이드라인을 제안한다.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.125-134
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• 2019

As the limitations of the passive recognition domain, which is not guaranteed transparency of the operation process, AI technology has a vulnerability that depends on the data. Human error is inherent because raw data for artificial intelligence learning must be processed and inspected manually to secure data quality for the advancement of AI learning. In this study, we examine the necessity of learning data management before machine learning by analyzing inaccurate cases of AI learning data and cyber security attack method through the approach from cyber security perspective. In order to verify the learning data integrity, this paper presents the direction of data-preserving artificial intelligence system, a blockchain-based learning data environment model. The proposed method is expected to prevent the threats such as cyber attack and data corruption in providing and using data in the open network for data processing and raw data collection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.447-463
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• 2022

Artificial Intelligence (AI) technology is a major technology that develops smart ships into autonomous ships in the marine industry. Autonomous ships recognize a situation with the information collected without human judgment which allow them to operate on their own. Existing ship systems, like control systems on land, are not designed for security against cyberattacks. As a result, there are infringements on numerous data collected inside and outside the ship and potential cyber threats to AI technology to be applied to the ship. For the safety of autonomous ships, it is necessary to focus not only on the cybersecurity of the ship system, but also on the cybersecurity of AI technology. In this paper, we analyzed potential cyber threats that could arise in AI technologies to be applied to existing ship systems and autonomous ships, and derived categories that require security risks and the security of autonomous ships. Based on the derived results, it presents future directions for cybersecurity research on autonomous ships and contributes to improving cybersecurity.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.61-68
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• 2024

In this paper, Generative AI is a technology that creates various forms of content such as text, images, and music, and is being utilized across different fields. In the security sector, generative AI is poised to open up new possibilities in various areas including security vulnerability analysis, malware detection and analysis, and the creation and improvement of security policies. This paper presents a guide for identifying vulnerabilities and secure coding using ChatGPT for security vulnerability analysis and prediction, considering the application of generative AI in the security domain. While generative AI offers innovative possibilities in the security field, it is essential to continuously pursue research and development to ensure safe and effective utilization of generative AI through in-depth consideration of ethical and legal issues accompanying technological advancements.

• Convergence Security Journal
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• v.23 no.5
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• pp.117-123
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• 2023

Recently, as the demand for Generative Artificial Intelligence (AI) and artificial intelligence has increased, the seriousness of misuse and abuse has emerged. However, intelligent CCTV, which maximizes detection of abnormal behavior, is of great help to prevent crime in the military and police. AI performs learning as taught by humans and then proceeds with self-learning. Since AI makes judgments according to the learned results, it is necessary to clearly understand the characteristics of learning. However, it is often difficult to visually judge strange and abnormal behaviors that are ambiguous even for humans to judge. It is very difficult to learn this with the eyes of artificial intelligence, and the result of learning is very many False Positive, False Negative, and True Negative. In response, this paper presented standards and methods for clarifying the learning of AI's strange and abnormal behaviors, and presented learning measures to maximize the judgment ability of intelligent CCTV's False Positive, False Negative, and True Negative. Through this paper, it is expected that the artificial intelligence engine performance of intelligent CCTV currently in use can be maximized, and the ratio of False Positive and False Negative can be minimized..

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.799-800
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• 2023

최근 많은 기업체들은 점점 고도화되고 있는 사이버 공격 위협에 대응하기 위해 다양한 보안 솔루션 도입 및 종합적인 네트워크 보안 분석을 수행하고 있다. 하지만 보안 영역에 많은 자원과 예산을 투입할 여력이 없는 중소형 업체들은 특히 침해위협 탐지와 대응 결과시각화에 대한 어려움을 겪고 있다. 이에 따라 본 연구에서는 다중 사이버 보안 AI 모델구현을 통해 다각도의 사이버 침해위협 발생 가능성을 예측하고, 추가적으로 오픈소스 기반의 ELK 플랫폼을 통한 대응 결과 시각화를 구현하고자 한다.

• Journal of Venture Innovation
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• v.6 no.4
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• pp.153-170
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• 2023

Generative AI services, including ChatGPT, were becoming increasingly active. This study aimed to empirically analyze the factors that promoted and hindered the diffusion of such services from a consumer perspective. Accordingly, a research model was developed based on the Value-based Adoption Model (VAM) framework, addressing both benefit and sacrifice factors. Benefits identified included usefulness and enjoyment, while sacrifices were security and hallucination. The study analyzed how these factors affected the intention to use generative AI services. A survey was conducted among college students for empirical analysis, and 200 valid responses were analyzed. The analysis utilized structural equation modeling with AMOS 24. The empirical results showed that usefulness and enjoyment had a significant positive impact on perceived value, while security and hallucination had a significant negative impact. The order of influence on perceived value was usefulness, hallucination, security, and then enjoyment. Perceived value had a significant positive impact on usage intention. Moreover, perceived value was found to mediate the relationship between usefulness, enjoyment, security, hallucination, and the intention to use generative AI services. These findings expanded the research horizon academically by validating the effectiveness of generative AI services based on existing models and demonstrated the continued importance of usefulness in a practical context.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1013-1016
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• 2012

IEEE 802.11 규격[1]은 2.4GHz 및 5GHz 대역에서 무선 근거리 접속 통신을 위한 국제 표준이다. 1997 년 2.4GHz 대역에서 1, 2 Mbps 의 속도를 지원하는 최초의 규격이 정의된 이래, 속도 개선을 위한 변복조 방식, 보안, QoS 등 다양한 요구 사항을 만족하기 위하여 표준이 지속적으로 개정되어 왔으며 2012 년 새로 개정된 표준이 발표된 바 있다. 특히, 최근 들어서 스마트폰의 무선랜 사용량이 폭발적으로 증가하고 무선랜 접속을 위한 핫스팟 역시 수가 크게 늘면서, 보안성을 유지하면서도 무선랜의 초기 연결접속 시간을 최소화(FILS; Fast Initial Link Setup)함으로써 무선랜 접속 요청 이용자 수에 확장성을 갖는 무선랜 규격을 제정할 필요성이 생기면서 IEEE 내에 802.11ai Task Group[3]이 승인되어 현재 표준화 작업을 진행중에 있다. IEEE 802.11 무선랜 규격에서 초기 연결접속 시간의 상당 부분을 네트워크 발견, 보안 접속, 인증 등에 소요하게 되어, IEEE 802.11ai에서는 보안성을 떨어뜨리지 않으면서도 빠르게 인증을 하기 위한 매커니즘에 대해 논의 중이다. 본 논문에서는 IEEE 802.11ai에서 논의 중인 "FILS를 위한 EAP/EAP-RP 기반의 빠른 인증" 기술에 대해 살펴보고, 이의 장단점을 분석하여 보다 개선된 형태의 빠른 인증 기법을 제안하고자 한다.

• Convergence Security Journal
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• v.23 no.2
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• pp.55-66
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• 2023

Recently, there is an increasing movement to increase the value of AI learning data and to secure high-quality data based on previous research on AI learning data in all areas of society. Therefore, quality management is very important in construction projects to secure high-quality data. In this paper, quality management to secure high-quality data when building AI learning data and improvement plans for each construction process are presented. In particular, more than 80% of the data quality of unstructured data built for AI learning is determined during the construction process. In this paper, we performed quality inspection of image/video data. In addition, we identified inspection procedures and problem elements that occurred in the construction phases of acquisition, data cleaning, labeling, and models, and suggested ways to secure high-quality data by solving them. Through this, it is expected that it will be an alternative to overcome the quality deviation of data for research groups and operators participating in the construction of AI learning data.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.97-105
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• 2022

This paper explains trends in security technologies for ICS. Since ICS is usually applied to large-scale national main infrastructures and industry fields, minor errors caused by cyberattack could generate enormous economic cost. ICS has different characteristic with commonly used IT systems, so considering security threats of ICS separately with IT is needed for developing modern security technology. This paper introduce framework for ICS that analyzes recent cyberattack tactics & techniques and find out trends in Intrusion Detection System (IDS) which is representative technology for ICS security, and analyzes AI technologies used for IDS. Specifically, this paper explains data collection and analysis for applying AI techniques, AI models, techniques for evaluating AI Model.