• 정보보호학회논문지
• /
• 제25권6호
• /
• pp.1399-1410
• /
• 2015

기업들은 다가오는 데이터 경쟁시대를 이해하고 이에 대비해야 한다며 가트너는 기업의 생존 패러다임에 많은 변화를 요구하고 있다. 또한 통계 알고리즘 기반의 예측분석을 통한 비즈니스 성공 사례들이 발표되면서, 과거 데이터 분석에 따른 사후 조치에서 예측 분석에 의한 선제적 대응으로의 전환은 앞서가고 있는 기업의 필수품이 되어 가고 있다. 이러한 경향은 보안 분석 및 로그 분석 분야에도 영향을 미치고 있으며, 실제로 빅데이터화되고 있는 대용량 로그에 대한 분석과 지능화, 장기화되고 있는 보안 분석에 빅데이터 분석 프레임워크를 활용하는 사례들이 속속 발표되고 있다. 그러나 빅데이터 로그 분석 시스템에 요구되는 모든 기능 및 기술들을 하둡 기반의 빅데이터 플랫폼에서 수용할 수 없는 문제점들이 있어서 독자적인 플랫폼 기반의 빅데이터 로그 분석 제품들이 여전히 시장에 공급되고 있다. 본 논문에서는 이러한 독자적인 빅데이터 로그 분석 시스템을 위한 실시간 및 비실시간 예측 분석 엔진을 탑재하여 사이버 공격에 선제적으로 대응할 수 있는 프레임워크를 제안하고자 한다.

• International Journal of Internet, Broadcasting and Communication
• /
• 제12권4호
• /
• pp.218-225
• /
• 2020

The purpose of this paper is to establish an IoT-based big data platform that can check the space and form analysis in various play cultures of children. Therefore, to this end, in order to understand the healthy play culture of children, we are going to build a big data platform that allows IoT and smart devices to work together to collect data. Therefore, the goal of this study is to develop a big data platform linked to IoT first in order to collect data related to observation of children's mobile movements. Using the developed big data platform, children's play culture can be checked anywhere through observation and intuitive UI design, quick information can be automatically collected and real-time feedback, data collected through repeaters can be aggregated and analyzed, and systematic database can be utilized in the form of big data.

• 전기학회논문지P
• /
• 제64권2호
• /
• pp.85-91
• /
• 2015

With the increasing popularity of Smart Grid infrastructure, it is much easier to collect energy usage data using AMI (Advanced Measuring Instrument) from residential housing, buildings and factories. Several researches have been done to improve an energy efficiency by analyzing the collected energy usage data. However, it is not easy to store and analyze the energy data using a traditional relational database management system since the data size grows exponentially with an increasing popularity of Smart grid infrastructure. In this paper, we are proposing a Hadoop based Big data system to store and analyze energy usage data. Based on our limited experiments, Hadoop based energy data analysis is three times faster than that of a relational database management system based approach with the current system.

• 한국군사과학기술학회지
• /
• 제21권5호
• /
• pp.714-721
• /
• 2018

This study proposes a risk evaluation method based on RAM and AHP data in order to prevent subjectivity of risk assessment. The risk assessment consist of Risk Likelihood(RL) and Risk Consequence(RC) in five levels. However, risk analysis of warships is hard to make a judgment because of small quantity production(Ship), long building period, equipment changes, complexity, various kinds of equipments, etc. The proposed RAM data and AHP analysis method are used to quantify each level quantitatively. RAM(MTBF) date is used to classify the RL, and AHP analysis is used to classify the RC. These scientific and data-based method will increase objectivity as well as efficiency of risk evaluation.

• 한국데이터정보과학회:학술대회논문집
• /
• 한국데이터정보과학회 2003년도 춘계학술대회
• /
• pp.97-108
• /
• 2003

Cluster analysis has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on on-line or off-line and so on. Clustering can identify dense and sparse regions among data attributes or object attributes. But it requires many hours to get clusters that we want, because of clustering is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new method of clustering using sample based on grid. It is more fast than any traditional clustering method and maintains its accuracy. It reduces running time by using grid-based sample. And other clustering applications can be more effective by using this methods with its original methods.

• International Journal of Computer Science & Network Security
• /
• 제21권9호
• /
• pp.103-108
• /
• 2021

The emergence of new technologies and their implementation by different manufacturers of electronic devices are experiencing an ascending trend. Most of the time, these protocols are expected to reach a certain degree of maturity, and electronic equipment manufacturers use simplified communication standards and interfaces that have already reached maturity in terms of their development such as ModBUS, KNX or CAN. This paper proposes an IoT solution of the Smart Home type based on an Analysis and Prediction System. A data acquisition component was implemented and there was defined an algorithm for the analysis and prediction of actions based on the values collected from the data update component and the data logger records.

• ETRI Journal
• /
• 제43권4호
• /
• pp.746-757
• /
• 2021

Side-channel attacks pose an inevitable challenge to the implementation of cryptographic algorithms, and it is important to mitigate them. This work identifies a novel data encoding technique based on 1-of-4 codes to resist differential power analysis attacks, which is the most investigated category of side-channel attacks. The four code words of the 1-of-4 codes, namely (0001, 0010, 1000, and 0100), are split into two sets: set-0 and set-1. Using a select signal, the data processed in hardware is switched between the two encoding sets alternately such that the Hamming weight and Hamming distance are equalized. As a case study, the proposed technique is validated for the NIST standard AES-128 cipher. The proposed technique resists differential power analysis performed using statistical methods, namely correlation, mutual information, difference of means, and Welch's t-test based on the Hamming weight and distance models. The experimental results show that the proposed countermeasure has an area overhead of 2.3× with no performance degradation comparatively.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제3권12호
• /
• pp.553-564
• /
• 2014

본 논문은 지금까지의 소셜미디어 분석과 분석보고서 생성의 세 가지 문제점을 해결하기 위해서 소셜 빅데이터 마이닝에 기반한 이슈분석보고서 자동 생성 시스템을 제안한다. 세 가지 문제점은 분석의 고립성, 전문가의 주관성과 고비용에 기인한 정보의 폐쇄성이다. 시스템은 자연언어 질의분석, 이슈분석, 소셜 빅데이터 분석, 소셜 빅데이터 상관성분석과 자동 보고서 생성으로 구성된다. 생성된 보고서의 유용성을 평가하기 위해, 본 논문에서는 리커트척도를 사용하였고, 빅데이터 분석 전문가 2명이 평가하였다. 평가결과는 리커트 척도 평가에서 보고서의 품질이 비교적 유용하고 신뢰할 수 있는 것으로 평가되었다. 보고서 생성의 저비용, 소셜 빅데이터의 상관성 분석과 소셜 빅데이터 분석의 객관성 때문에, 제안된 시스템이 소셜 빅데이터 분석의 대중화를 선도할 것으로 기대된다.

• 한국측량학회지
• /
• 제33권3호
• /
• pp.211-218
• /
• 2015

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

• 한국안전학회지
• /
• 제37권2호
• /
• pp.18-27
• /
• 2022

Construction accidents are difficult to prevent because several different types of activities occur simultaneously. The current method of accident analysis only indicates the number of occurrences for one or two variables and accidents have not reduced as a result of safety measures that focus solely on individual variables. Even if accident data is analyzed to establish appropriate safety measures, it is difficult to derive significant results due to a large number of data variables, elements, and qualitative records. In this study, in order to simplify the analysis and approach this complex problem logically, data preprocessing techniques, such as latent class cluster analysis (LCCA) and predictor importance were used to discover the most influential variables. Finally, the correlation was analyzed using an alluvial flow diagram consisting of seven variables and fourteen elements based on accident data. The alluvial diagram analysis using reduced variables and elements enabled the identification of accident trends into four categories. The findings of this study demonstrate that complex and diverse construction accident data can yield relevant analysis results, assisting in the prevention of accidents.