Blacklist를 활용한 선택적 평문 충돌 쌍 공격

김은희(Eun-hee Kim),김태원(Tae-won Kim),홍석희(Seok-hie Hong) 한국정보보호학회 2014 정보보호학회논문지 Vol.24 No.6

부채널을 이용한 충돌 쌍 공격은 부채널 신호를 통해 동일한 비밀 중간 값을 확인하고, 이를 이용하여 암호 알고리즘의 비밀정보를 복원한다. CHES 2011에서 Clavier 등은 Blacklist를 활용하여 기존보다 적은 수의 평문으로 충돌 쌍 공격을 수행하였다. 하지만 Blacklist를 활용한 구체적인 방법 또는 수행 알고리즘에 대한 언급이 없고 단지 사용된 평문의 수치만 소개하였다. 따라서 본 논문에서는 효율적인 충돌 쌍 공격을 위한 구체적인 방법을 소개한다. 우선 Blacklist를 활용할 수 있도록 기본적인 개념 및 용어, 표기법을 정의했으며, 이를 바탕으로 여러 가지 기법들을 제안했다. 또한 설계 시 공격 성능에 가장 큰 영향을 주는 사항에 대해 중점적으로 기술하였고, 분석을 통해 좀 더 효율적인 알고리즘을 설계함으로써 성능 향상을 꾀하였다. Collision attacks using side channel analysis confirm same intermediate value and restore sensitive data of algorithm using this point. In CHES 2011 Clavier and other authors implemented the improved attack using Blacklist so they carried out the attack successfully using less plaintext than before. However they did not refer the details of Blacklist method and just performed algorithms with the number of used plaintext. Therefore in this paper, we propose the specific method to carry out efficient collision attack. At first we define basic concepts, terms, and notations. And using these, we propose various methods. Also we describe facts that greatly influence on attack performance in priority, and then we try to improve the performance of this attack by analyzing the algorithm and structuring more efficient one.

증보판 - 보문 : 20세기에 나타난 여성의 이상적 신체미와 패션에 관한 연구

김은희 ( Eun Hie Kim ) 한국의류학회 2001 한국의류학회지 Vol.25 No.2

기상요인이 의류제품 판매량에 미치는 영향 - F/W 판매데이터(9월~익년 2월)를 근거로 -

김은희 ( Eun Hie Kim ),황보현우 ( Hyunwoo Hwangbo ),채진미 ( Jin Mie Chae ) 복식문화학회 2017 服飾文化硏究 Vol.25 No.2

The purpose of this study was to investigate meteorological factors` effects on clothing sales based on empirical data from a leading apparel company. The daily sales data were aggregated from “A” company`s store records for the Fall/Winter season from 2012 to 2015. Daily weather data corresponding to sales volume data were collected from the Korea Meteorological Administration. The weekend effect and meteorological factors including temperature, wind, humidity, rainfall, fine dust, sea level pressure, and sunshine hours were selected as independent variables to calculate their effects on A company`s apparel sales volume. The analysis used a SAS program including correlation analysis, t-test, and multiple-regression analysis. The study results were: First, the weekend effect was the most influential factor affecting sales volume, followed by fine dust and temperature. Second, there were significant differences in the independent variables`effects on sales volume according to the garments` classification. Third, temperature significantly affected outer garments`sales volume, while top garments` sales volume was not influenced significantly. Fourth, humidity, sea level pressure and sunshine affected sales volume partly according to the garments` item. This study can provide proof of significant relationships between meteorological factors and the sales volume of garments, which will serve well to establish better inventory strategies.

기상요인, 가격할인 및 주말효과가 의류상품 판매량에 미치는 영향

황보현우,김은희,채진미,Hwangbo, Hyunwoo,Kim, Eun Hie,Chae, Jin Mie 한국의류산업학회 2017 한국의류산업학회지 Vol.19 No.4

This study investigated the effects of influencing factors on the sales volume of apparel products. Based on previous studies, weekend effect, discount rate, and meteorological factors including daily average temperature, rainfall, sea level pressure, and fine dust were selected as independent variables to calculate their effects on sales quantity of apparel products. The daily sales data during 2015 - 2016 were collected from casual brands and outdoor brands which "A" apparel manufacturing company had operated. The actual data of "A" company were analyzed using SAS(R) 9.4 and SAS(R) Enterprise Miner 14.1. The results of this study were as follows: First, the influencing factors on total sales volume of apparel products were proved to be the weekend effect, discount rate, and fine dust. Second, the analysis of influencing factors on sales volume of apparel products according to season showed: 1) In casual brands, the average temperature had a significant influence on the sales volume of spring/summer products, and the sea level pressure affected the sales volume of summer/fall/winter products significantly. 2) In outdoor brands, the average temperature and the fine dust had a significant influence on the sales volume of all season's products. The sea level pressure affected the sales volume of summer/fall/ winter products significantly. The weekend effect and the discount effect affected the sales volume of apparel products partly. Third, the effect of rainfall was not proven significant, which was different from the results of past studies.

티셔츠 상품의 판매패턴과 연관된 상품속성

채진미 ( Jin Mie Chae ),김은희 ( Eun Hie Kim ) 한국의류학회 2020 한국의류학회지 Vol.44 No.6

This study examined the sales pattern relationship with respect to product attributes to propose sales forecasting for fashion products. We analyzed 537 SKU sales data of T-shirts in the domestic sports brand using SAS program. The sales pattern of fashion products fluctuated and were influenced by exogenous factors; therefore, we removed the influence of exogenous factors found to be price discounts and holiday effects as a result of regression analysis. In addition, it was difficult to predict sales using the sales patterns of the same product since fashion products were released as new products every year. Therefore, the forecasting model was proposed using sales patterns of related product attributes when attributes were considered descriptive variables. We classified sales patterns using K-means clustering in order to explain the relationship between sales patterns and product attributes along with creating a decision tree classifier using attributes as input and sales patterns as output. As a result, the sales patterns of T-shirts were clustered into six types that featured the characteristic shape of peak and slope. It was also associated with the combination of product attributes and their values in regards to the proposed sales pattern prediction model.

머신 러닝을 활용한 의류제품의 판매량 예측 모델- 아우터웨어 품목을 중심으로 -

채진미 ( Jin Mie Chae ),김은희 ( Eun Hie Kim ) 한국의류산업학회 2021 한국의류산업학회지 Vol.23 No.4

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.