• 한국정보과학회논문지:정보통신
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• 제29권2호
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• pp.109-116
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• 2002

그룹에 기반한 통신 응용 서비스의 요구가 증가함에 따라 멀티캐스트 기반 구조에 대한 연구가 활발히 진행되고 있다. 하지만 멀티캐스트 구조에 대한 안전성과 효율성 및 확장성 부분에 대한 해결책은 아직 미비한 상태이다. 본 연구에서는 기존의 대표적인 멀티캐스트 키 관리 구조를 고찰함과 동시에 PKI(Public Key Infrastructure), Ipsec, 도메인 Subgroup 및 구조적 이원화 기법 등에 기초하여 확장성을 제공하는 안전한 멀티캐스트 키 관리 구조를 제안한다. 또한 새로이 제안된 방식과 기존의 방식들을 안전성, 효율성 및 확장성 부분에서 비교 분석함으로서 그 효용성을 검증한다.

• 한국컴퓨터산업학회논문지
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• 제6권3호
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• pp.457-464
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• 2005

정보통신 기술의 비약적인 발전으로 인해 인터넷은 필수 불가결한 도구가 되고 있다. 이러한 정보화 시대의 요구에 대한 교육적 대응은 학습자중심의 교육이며, 정보통신 기술을 기반으로 한 원격 교육이다. 그와 더불어 차세대 웹 표준문서 포맷으로 부상되고 있는 XML(eXtensible Markup Language)을 사용한 규격에 대한 국내외적인 표준화 작업 또한 가속화되고 있으며, 최근 XML 보안에 대한 연구가 활성화되고 있다. 하지만 2004년부터 사용자들은 CA를 통해 인증을 받으려고 하면 인증서비스에 대한 지불을 해야 하는 단점이 있다. 본 논문에서는 기존의 원격교육 사이트에 다중인증 기법을 적용하여 가입시에 공인인증서를 한번 받도록 하며, 본 시스템에서 제안하는 XML 전자서명을 발급받아 보안성을 유지할 수 있는 방법을 제안하고 이에 대한 시스템 구현을 통해 해결하고자 한다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제5권2호
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• pp.41-46
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• 2016

최근 스마트폰 기술의 빠른 발전과 핀테크의 등장으로 스마트폰은 더욱 많은 민감한 정보를 다루게 되었다. 이 같은 민감한 정보를 안전하게 보호하는 수단으로 바이오인식 기술이 적용된 다양한 기기들이 출시되고 있으나, 바이오인식 시스템 설계 시 보안을 고려하지 않을 경우 잠재적인 취약점이 존재할 수 있다. 이에 본 논문에서는 잠재적인 취약점의 분석을 통해 스마트폰 상의 바이오인식 시스템 설계 과정에서 주의할 점을 분류하고, 이를 해결하기 위한 설계 요구사항을 제시한다. 또한, 설계 요구사항을 종합하여 안전한 스마트폰 바이오인식시스템 설계를 위한 프레임워크를 제시한다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.878-881
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• 2009

보안 관련 설계 기술 개발에 대해서는 국내와 국외의 현황이 거의 차이가 나지 않는다. 현재 2048 비트 RSA 처리 모듈이 개발되고 있는 추세이긴 하지만 처리 비트폭이 넓은 이유로 연산 처리 속도가 빠르지 않아 효율적 자원을 소모하면서 고속으로 동작되는 RSA 처리부의 설계가 필요하다. RNG (Random Number Generator) 개발 측면에서는 PRNG (Pseudo Random Number Generator)에서 TRNG (True Random Number Generator)로 바뀌는 추세이며 소면적 고속의 전용 RNG가 요구된다. 칩 레벨 보안 관련해서는 국내외 제조사별로 특허권 침해를 받지 않는 보안 칩 고유의 안전장치를 개발하고 있으며, 독자적인 칩 레벨의 안전장치가 필요하다.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.303-316
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• 2021

Digital content protection has recently become an important requirement in biometrics-based authentication systems due to the challenges involved in designing a feasible and effective user authentication method. Biometric approaches are more effective than traditional methods, and simultaneously, they cannot be considered entirely reliable. This study develops a reliable and trustworthy method for verifying that the owner of the biometric traits is the actual user and not an impostor. Watermarking-based approaches are developed using a combination of a color face image of the user and a mobile equipment identifier (MEID). Employing watermark techniques that cannot be easily removed or destroyed, a blind image watermarking scheme based on fast discrete curvelet transform (FDCuT) and discrete cosine transform (DCT) is proposed. FDCuT is applied to the color face image to obtain various frequency coefficients of the image curvelet decomposition, and for high frequency curvelet coefficients DCT is applied to obtain various frequency coefficients. Furthermore, mid-band frequency coefficients are modified using two uncorrelated noise sequences with the MEID watermark bits to obtain a watermarked image. An analysis is carried out to verify the performance of the proposed schema using conventional performance metrics. Compared with an existing approach, the proposed approach is better able to protect multimedia data from unauthorized access and will effectively prevent anyone other than the actual user from using the identity or images.

• 융합보안논문지
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• 제20권4호
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• pp.177-186
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• 2020

현재의 퍼블릭 블록체인은 누구나 원장의 내용을 볼 수 있도록 설계가 되어있다. 하지만 응용에 따라서 비밀 정보를 블록체인에 저장해야 하는 경우도 있으나 이에 대한 연구는 아직 미진하다. 본 논문에서는 DPoS(Delegated Proof of Stack) 합의방식을 사용하는 블록체인을 대상으로 공개 블록과 비공개 블록의 두 계층으로 이루어진 블록체인을 제안하고 비공개 블록의 암호화를 위한 요구사항을 도출하였다. 도출된 암호화 요구사항을 만족하는 dealer없는 t-of-n threshold 암호화를 제안하였다. 또한, DPoS의 대표노드들은 가입과 탈퇴가 발생할 수 있어서, 대표노드의 가입과 탈퇴에 따라서 키 조각을 재분배하는 효율적인 방법을 제시하였다. 제안된 기법이 대표노드간의 공평성과 동일한 신뢰성을 만족하는 특징을 가진다.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.400-407
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• 2022

Innovations significantly affect the efficiency of the socioeconomic systems of the regions, acting as a system-forming element of their development. Modern models of economic development also consider innovation activity, intellectual potential, knowledge as the basic factors for stimulating the economic growth of the region. The purpose of the study is to develop methodological foundations for evaluating the effectiveness of a regional innovation system based on a multidimensional analysis of its effects. To further study the effectiveness of RIS, we have used one of the methods of multidimensional statistical analysis - canonical analysis. The next approach allows adding another important requirement to the methodological provision of evaluation of the level of innovation development of industries and regions, namely - the time factor, the formalization of which is realized in autoregressive dynamic economic and mathematical models and can be used in our research. Multidimensional Statistical Analysis for RIS effectiveness estimation was used to model RIS by typological regression. Based on it, multiple regression models were built in groups of regions with low and relatively high innovation potential. To solve the methodological problem of RIS research, we can also use the approach to the system as a "box" with inputs and outputs.