• Journal of Information Technology Services
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• v.21 no.1
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• pp.81-102
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• 2022

Although more than 99% of all Korean companies are small and medium-sized enterprises (SMEs), which accounts for a large part of the national economy, they are having difficulties in securing information protection capabilities due to problems such as budget and manpower. On the other hand, as 97% of cyber incidents are concentrated in SMEs, it is urgent to strengthen the information protection management and response capabilities of SMEs. Although the government is promoting company-wide information security consulting for SMEs, the need for supplementing it's procedures and consulting items is being raised. Based on the results of information security consulting supported by the government in 2020, this study attempted to derive improvement plans by interviewing SME workers, information security consultants, and system operators. Through the research results, it is expected to create a basis for SMEs to autonomously check the information security management system and contribute to the reference of related policies.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.85-87
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• 2022

최근 CCTV 출입 기록, 휴대폰 보안, 스마트 매장 등에서 얼굴 인식을 통해 개인을 식별하는 기술이 널리 사용되고 있다. 카메라의 각도, 조명, 사람의 움직임 등 얼굴 인식에 많은 외부 환경이 영향을 미치고 있지만 그중에서도 실제 영상에서 얼굴이 차지하는 영역이 작아 저해상도 얼굴 인식에 어려움을 겪고 있다. 이러한 문제점을 해결하고자 본 논문에서는 이미지 해상도가 얼굴 인식에 끼치는 영향을 알아보고 이미지 초해상화를 통해 얼굴 인식 성능을 개선하고자 한다. 쌍선형, 양3차 회선 보간법과 딥러닝 기반의 이미지 초해상화 모델인 RCAN을 이용하여 업스케일링한 데이터셋에 대해 학습한 ArcFace를 통해 얼굴 검증 평가를 진행하였다. 고해상도 이미지는 얼굴 인식 성능을 향상시키며, RCAN을 사용한 이미지 초해상화가 보간법을 사용한 방법보다 더 좋은 성능을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.472-475
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• 2020

실생활의 사례를 바탕으로 생성된 여러 분야의 데이터셋을 기계학습 (Machine Learning) 문제에 적용하고 있다. 정보보안 분야에서도 사이버 공간에서의 공격 트래픽 데이터를 기계학습으로 분석하는 많은 연구들이 진행 되어 왔다. 본 논문에서는 공격 데이터를 유형별로 정확히 분류할 때, 실생활 데이터에서 흔하게 발생하는 데이터 불균형 문제로 인한 분류 성능 저하에 대한 해결방안을 연구했다. 희소 클래스 관점에서 데이터를 재구성하고 기계학습에 악영향을 끼치는 특징들을 제거하고 DNN(Deep Neural Network) 모델을 사용해 분류 성능을 평가했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.254-257
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• 2024

5G 통신과 인공지능 기술이 발전하고, 사물인터넷 기기의 수가 증가함에 따라 종래의 정보보호체계를 우회하는 지능적인 사이버 공격이 증가하고 있다. 그러나, 종래의 기계학습 기반 멀웨어 탐지 방식은 이미 알려진 멀웨어만 탐지할 수 있으며, 새로운 멀웨어는 탐지가 어렵거나, 기존의 알려진 멀웨어로 잘못 분류되는 문제가 있다. 본 연구에서는 비지도학습을 사용하여 알려지지 않은 멀웨어를 탐지하고, 새롭게 탐지된 멀웨어를 새로운 라벨로 분류하여 재학습하는 준지도 학습 기반의 멀웨어 탐지 기법을 제안한다. 다양한 데이터 환경에서 알려지지 않은 멀웨어 데이터가 탐지 모델로 입력될 때 제안한 방식의 성능을 평가했다. 실험 결과에 따르면 제안한 준지도 학습 기반의 멀웨어 탐지 방법은 종래의 방식 대비 정확도를 약 16% 개선했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.745-748
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• 2024

글로벌 시장에서 Large Language Model(LLM)의 발전이 급속하게 이루어지며 활용도가 높아지고 있지만 특정 유형이나 전문적 지식이 부족할 수 있어 일반화하기 어려우며, 새로운 데이터로 업데이트하기 어렵다는 한계점이 있다. 이를 극복하기 위해 지속적으로 업데이트되는 최신 정보를 포함한 외부 데이터베이스에서 정보를 검색해 응답을 생성하는 Retrieval- Augmented Generation(RAG, 검색 증강 생성) 모델을 도입하여 LLM의 환각 현상을 최소화하고 효율성과 정확성을 향상시키려는 연구가 활발히 이루어지고 있다. 본 논문에서는 LLM의 검색 기능을 강화하기 위한 RAG의 연구 및 평가기법에 대한 최신 연구 동향을 소개하고 실제 산업에서 활용하기 위한 최적화 및 응용 사례를 소개하며 이를 바탕으로 향후 연구 방향성을 제시하고자 한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1217-1224
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• 2015

Web application security problems are addressed by the web vulnerability analysis which in turn supports companies to understand those problems and to establish their own solutions. Ministry of Science, ICT and Future Planning (MSIP) has released its guidelines for analysis and assessment of the web vulnerability. Although it is possible to distinguish vulnerability items in a manner suggested in the MSIP's guidelines, MSIP's factors and criteria proposed in the guidelines are neither sufficient nor efficient in analyzing specific vulnerability entries' risks. This study discusses analysis of the domestic and international Vulnerability Scoring system and proposes an appropriate evaluating method for web vulnerability analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.12-18
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• 2011

In this paper, we proposed Interval Type-2 polynomial Radial Basis Function Neural Networks. In the receptive filed of hidden layer, Interval Type-2 fuzzy set is used. The characteristic of Interval Type-2 fuzzy set has Footprint Of Uncertainly(FOU), which denotes a certain level of robustness in the presence of un-known information when compared with the type-1 fuzzy set. In order to improve the performance of proposed model, we used the linear polynomial function as connection weight of network. The parameters such as center values of receptive field, constant deviation, and connection weight between hidden layer and output layer are optimized by Conjugate Gradient Method(CGM) and Space Search Evolutionary Algorithm(SSEA). The proposed model is applied to gas furnace dataset and its result are compared with those reported in the previous studies.

• Smart Media Journal
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• v.12 no.7
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• pp.59-67
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• 2023

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The collected dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.99-110
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• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.

• Proceedings of the Korea Information Assurance Society Conference
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• 2004.05a
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• pp.259-265
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• 2004

Since the 2003.1.25 Internet Crisis, the government has been looking at a number of options to strengthen national cyber-security/crisis management capability to guard against ever increasing threat of cyber-war and terror. Thus, the focus of this study was to explorer new ways of developing a comprehensive cyber-security/crisis management system, in particular by combining modern social-science analytic theories. As a result, although there has to be more in-depth researches into incorporating advanced techniques to generate more detailed and object-specific indexes and protocols, the use of 'event data system,' which has been widely utilized in many recent social science researches to assess a wide-range of socio-political risks and crises, could be adopted as a basis for a comprehensive nationwide cyber-risk management system.