• Proceedings of the Korea Contents Association Conference
• /
• 2004.11a
• /
• pp.30-35
• /
• 2004

The objective of this research is to analyse the effect of family restaurant quality on the customer satisfaction level, by surveying college students who have used family restaurants. As these days well developed industrial society, not only service business but also most of business area needs to high quality service. For survey results, this study analyzed by statistical methods such as frequency analysis, factor analysis, and regression analysis.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.319-321
• /
• 2004

제품 계열 공학(Product Line Engineering, PLE)는 패밀리 멤버들의 공통성과 가변성을 분석하여 만든 핵심 자산을 특화시켜 어플리케이션을 개발함으로써 재사용성과 이용가능성을 증대시키는 접근 방법이다. 핵심 자산은 제품 계열에 속하는 패밀리 멤버들이 어플리케이션을 만드는데 기초가 되는 모든 자산을 포함하며, 아키텍처, 컴포넌트 둥이 포함될 수 있다. 범용 아키텍처는 패밀리 멤버들이 공통적으로 사용할 수 있는 아키텍처로, 제품 계열에 속하는 제품들의 구조를 정의하고 컴포넌트의 인터페이스 명세를 제공하여 컴포넌트만큼 중요한 재사용 단위이다. 본 논문에서는 대표적인 PLE 방법론에서 정의한 제품 계열 아키텍처와 일반 소프트웨어 아키텍처를 비교하여 범용 아키텍처에 포함되는 요소들을 선정하고, 메타 모델을 이용하여 범용 아키텍처 구성요소와 구성요소간 관계를 명확히 정의함으로써, 개념적인 아키텍처를 보다 실용적으로 설계하는데 도움이 되게 하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.05a
• /
• pp.212-215
• /
• 2021

본 논문에서는 딥러닝의 CNN(Convolution Neural Network) 학습을 통하여 악성코드를 실행시키지 않고서 악성코드 변종을 패밀리 그룹으로 분류하는 방법을 연구한다. 먼저 데이터 전처리를 통해 3가지의 서로 다른 방법으로 악성코드 이미지와 메타데이터를 생성하고 이를 CNN으로 학습시킨다. 첫째, 악성코드의 byte 파일을 8비트 gray-scale 이미지로 시각화하는 방법이다. 둘째, 악성코드 asm 파일의 opcode sequence 정보를 추출하고 이를 이미지로 변환하는 방법이다. 셋째, 악성코드 이미지와 메타데이터를 결합하여 분류에 적용하는 방법이다. 이미지 특징 추출을 위해서는 본고에서 제안한 CNN을 통한 학습 방식과 더불어 3개의 Pre-trained된 CNN 모델을 (InceptionV3, Densnet, Resnet-50) 사용하여 전이학습을 진행한다. 전이학습 시에는 마지막 분류 레이어층에서 본 논문에서 선택한 데이터셋에 대해서만 학습하도록 파인튜닝하였다. 결과적으로 가공된 악성코드 데이터를 적용하여 9개의 악성코드 패밀리로 분류하고 예측 정확도를 측정해 비교 분석한다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.3
• /
• pp.617-623
• /
• 2018

This study focuses on detection of malicious code through AndroidManifest permissoion feature extracted based on Android static analysis. Features are built on the permissions of AndroidManifest, which can save resources and time for analysis. Malicious app detection model consisted of SVM (support vector machine), NB (Naive Bayes), Gradient Boosting Classifier (GBC) and Logistic Regression model which learned 1,500 normal apps and 500 malicious apps and 98% detection rate. In addition, malicious app family identification is implemented by multi-classifiers model using algorithm SVM, GPC (Gaussian Process Classifier) and GBC (Gradient Boosting Classifier). The learned family identification machine learning model identified 92% of malicious app families.

• The Journal of the Korea Contents Association
• /
• v.11 no.3
• /
• pp.418-427
• /
• 2011

The purpose of this study is to investigate demand-determinant factors based on the number of visits and substitutive relations inter-restaurants, which are four major domestic family restaurants. Findings indicate that the factors of demand-determinant for visiting are affected by demographic characteristics, brand images of family restaurants, and the rate of the number of visits. In addition, this study used partial co-relation analysis to determine the substitutive relations of competitive restaurants. Considering these results, this study suggests how family restaurants' marketing strategy could be differentiated by discriminating the determinant factors which affect the number of visits. Also, this study makes it possible to arrange the opportunity to strengthen restaurants' competitiveness by examining competitive relations to the inter-restaurants.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.3
• /
• pp.531-539
• /
• 2019

This paper proposes the training features for malware family analysis and analyzes the multi-classification performance of ensemble models. We construct training data by extracting API and DLL information from malware executables and use Random Forest and XGBoost algorithms which are based on decision tree. API, API-DLL, and DLL-CM features for malware detection and family classification are proposed by analyzing frequently used API and DLL information from malware and converting high-dimensional features to low-dimensional features. The proposed feature selection method provides the advantages of data dimension reduction and fast learning. In performance comparison, the malware detection rate is 93.0% for Random Forest, the accuracy of malware family dataset is 92.0% for XGBoost, and the false positive rate of malware family dataset including benign is about 3.5% for Random Forest and XGBoost.

• Proceedings of the Culinary Society of Korean Academy Conference
• /
• 2004.11a
• /
• pp.209-224
• /
• 2004

• Proceedings of KHEA Conference
• /
• 2005.04a
• /
• pp.77-78
• /
• 2005

• Culinary science and hospitality research
• /
• v.20 no.4
• /
• pp.266-279
• /
• 2014

The purposes of the study are to segment the American family restaurant market based on relational benefits and to compare each group's demographics, dining characteristics, relationship quality(consumer identification, switching costs, satisfaction, commitment), and relational outcomes(positive word-of-mouth intentions and share of purchases). 510 responses were collected from American family restaurant customers and analyzed using frequency analysis, EFA, reliability test, cluster analysis, MANOVA, discriminant analysis, chi-square test, and ANOVA. The results of the study found three different types of relational benefits: confidence, special treatment, and social benefits. The results of cluster analysis identified three market segments, namely, high relational benefits consumers, medium relational benefits consumers, and low relational benefits consumers. The three groups were different in terms of age(p<0.05) and level of education(p<0.05). In addition, high relational benefits consumers showed a higher level of relationship frequency(p<0.001), relationship quality(p<0.001), and relational outcomes(p<0.001), followed by medium and low relational benefits consumers. Overall, the results indicated that family restaurants need to deliver excellent relational benefits to customers in order to achieve desired relationship quality and relational outcomes. Managerial implications were provided.

• Journal of Digital Convergence
• /
• v.14 no.7
• /
• pp.103-115
• /
• 2016

This study examines the relationship among family restaurants' service guarantee, service quality, service value, customer satisfaction and customer loyalty, involvement to verify the marketing effectiveness of service guarantee in family restaurants. For the empirical analysis, a survey was conducted with 250 adults in Seoul and Gyeonggi. For the collected data, frequency analysis, reliability analysis, factor analysis were carried out, using SPSS 21.0, in order to verify reliability and validity. And multi regression analysis, hierarchical analysis were used for hypothesis test. According to this study result, first, service guarantees were shown to have a positive effect on service quality, service value and customer loyalty. Second, service quality had a positive effect on customer satisfaction. Third, service value had a positive effect on customer satisfaction. Fourth, customer satisfaction had a positive effect on customer loyalty. Fifth, in the relationship between service guarantees and customer loyalty, involvement played a moderating role. Therefore this study has verified the marketing effectiveness of service guarantee in family restaurants, so it can be said that the study has drawn strategic operation methods in family restaurants.