• Title/Summary/Keyword: multimedia data mining

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Detection of Malicious Code using Association Rule Mining and Naive Bayes classification (연관규칙 마이닝과 나이브베이즈 분류를 이용한 악성코드 탐지)

  • Ju, Yeongji;Kim, Byeongsik;Shin, Juhyun
    • Journal of Korea Multimedia Society
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    • v.20 no.11
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    • pp.1759-1767
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    • 2017
  • Although Open API has been invigorated by advancements in the software industry, diverse types of malicious code have also increased. Thus, many studies have been carried out to discriminate the behaviors of malicious code based on API data, and to determine whether malicious code is included in a specific executable file. Existing methods detect malicious code by analyzing signature data, which requires a long time to detect mutated malicious code and has a high false detection rate. Accordingly, in this paper, we propose a method that analyzes and detects malicious code using association rule mining and an Naive Bayes classification. The proposed method reduces the false detection rate by mining the rules of malicious and normal code APIs in the PE file and grouping patterns using the DHP(Direct Hashing and Pruning) algorithm, and classifies malicious and normal files using the Naive Bayes.

A novel on Context Information Analysis and Prediction Process using Text Mining (텍스트 마이닝을 이용한 상황 정보 분석 및 예측 프로세스에 관한 연구)

  • Jung, Se-hoon;Kang, Joo-hee;Kim, Jong-chan;Sim, Chun-bo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2015.10a
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    • pp.1039-1040
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    • 2015
  • 최근 IoT 및 인공지능 기술을 활용한 상황 정보 예측 서비스가 각광을 받고 있다. 본 논문에서는 특정 메타 데이터(Meta Data)로부터 입력되는 정보를 기반으로 상황 정보 분석 및 예측하는 프로세스를 제안한다. 주성분 분석 및 데이터의 집단화(Corpus), 문서 매트릭스(Document Matrix), 단어 빈도수(Frequency)에 따른 데이터 전처리 과정을 통해 상황정보 데이터를 확보한다. 또한 연관 규칙분석을 통해 분류된 데이터의 연관성을 분석하여 예측 데이터의 연관성을 확보한다. 제안하는 상황정보 분석 및 예측 모델은 R을 적용하여 설계한다.

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Application of Laser Scanner for Mine Management and Mining Plan (광산관리와 채굴계획 수립을 위한 레이저스캐너의 활용)

  • Park, Joon Kyu;Jung, Kap Yong
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.7 no.6
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    • pp.693-700
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    • 2017
  • The mines in our country are complex in geography and shape and because of its small scale, accurate surveying performance and 3D modeling are necessary for mine development and management and mining plans. However, due to the data acquisition and processing technology and economy, the existing methods are currently used. The structure, mining, and mining area of the mine are recorded and managed based on the 2D drawings. As a result, it is true that there is risk of accidents caused by problems of accuracy as well as waste of personnel and time. In recent years, research data on geology and geospatial information on mines have been integrated into a database in foreign countries, and they are used for mine management and mining planning. In this study, we tried to construct spatial information for mining management and mining plan using laser scanner. Through research, spatial information about the mine was effectively obtained and produced data modeled through data processing. The 3D model for mining mines is expected to be a valuable tool for establishing and operating a safe mining plan for mines.

Methodological Issues in Internet Survey and Development of Personalized Internet Survey System Using Data Mining Techniques (인터넷 설문조사의 방법론적인 문제점과 데이터마이닝 기법을 활용한 개인화된 인터넷설문조사 시스템의 구축)

  • 김광용;김기수
    • Journal of Korean Society for Quality Management
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    • v.32 no.2
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    • pp.93-108
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    • 2004
  • The purpose of this research is to summarize the methodological issues in internet survey and to suggest personalized internet survey system using data mining technique for enhancing the survey quality of internet survey as well as utilizing the benefit of interactive multimedia factors of internet survey. The data mining technique used in this paper is Case Based Reasoning for adopting individual design preference affecting survey quality. For achieving the research purpose, two surveys, pre & post survey, were performed. Pre survey was done for implementing CBR database to find individual index affecting survey quality and post survey was used for measuring the peformance of personalized internet survey system. The result shows that the survey quality of personalized web survey system is better than generalized web survey system.

A Comparative Study on Discretization Algorithms for Data Mining (데이터 마이닝을 위한 이산화 알고리즘에 대한 비교 연구)

  • Choi, Byong-Su;Kim, Hyun-Ji;Cha, Woon-Ock
    • Communications for Statistical Applications and Methods
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    • v.18 no.1
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    • pp.89-102
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    • 2011
  • The discretization process that converts continuous attributes into discrete ones is a preprocessing step in data mining such as classification. Some classification algorithms can handle only discrete attributes. The purpose of discretization is to obtain discretized data without losing the information for the original data and to obtain a high predictive accuracy when discretized data are used in classification. Many discretization algorithms have been developed. This paper presents the results of our comparative study on recently proposed representative discretization algorithms from the view point of splitting versus merging and supervised versus unsupervised. We implemented R codes for discretization algorithms and made them available for public users.

Design and Implementation of an Interestingness Analysis System for Web Personalizatoion & Customization

  • Jung, Youn-Hong;Kim, I-I;Park, Kyoo-seok
    • Journal of Korea Multimedia Society
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    • v.6 no.4
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    • pp.707-713
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    • 2003
  • Convenience and promptness of the internet have been not only making the electronic commerce grow rapidly in case of website, analyzing a navigation pattern of the users has been also making personalization and customization techniques develop rapidly for providing service accordant to individual interestingness. Web personalization and customization skill has been utilizing various methods, such as web log mining to use web log data and web mining to use the transaction of users etc, especially e-CRM analyzing a navigation pattern of the users. In this paper, We measure exact duration time of the users in web page and web site, compute weight about duration time each page, and propose a way to comprehend e-loyalty through the computed weight.

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Analysis on Review Data of Restaurants in Google Maps through Text Mining: Focusing on Sentiment Analysis

  • Shin, Bee;Ryu, Sohee;Kim, Yongjun;Kim, Dongwhan
    • Journal of Multimedia Information System
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    • v.9 no.1
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    • pp.61-68
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    • 2022
  • The importance of online reviews is prevalent as more people access goods or places online and make decisions to visit or purchase. However, such reviews are generally provided by short sentences or mere star ratings; failing to provide a general overview of customer preferences and decision factors. This study explored and broke down restaurant reviews found on Google Maps. After collecting and analyzing 5,427 reviews, we vectorized the importance of words using the TF-IDF. We used a random forest machine learning algorithm to calculate the coefficient of positivity and negativity of words used in reviews. As the result, we were able to build a dictionary of words for positive and negative sentiment using each word's coefficient. We classified words into four major evaluation categories and derived insights into sentiment in each criterion. We believe the dictionary of review words and analyzing the major evaluation categories can help prospective restaurant visitors to read between the lines on restaurant reviews found on the Web.

Video Ranking Model: a Data-Mining Solution with the Understood User Engagement

  • Chen, Yongyu;Chen, Jianxin;Zhou, Liang;Yan, Ying;Huang, Ruochen;Zhang, Wei
    • Journal of Multimedia Information System
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    • v.1 no.1
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    • pp.67-75
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    • 2014
  • Nowadays as video services grow rapidly, it is important for the service providers to provide customized services. Video ranking plays a key role for the service providers to attract the subscribers. In this paper we propose a weekly video ranking mechanism based on the quantified user engagement. The traditional QoE ranking mechanism is relatively subjective and usually is accomplished by grading, while QoS is relatively objective and is accomplished by analyzing the quality metrics. The goal of this paper is to establish a ranking mechanism which combines the both advantages of QoS and QoE according to the third-party data collection platform. We use data mining method to classify and analyze the collected data. In order to apply into the actual situation, we first group the videos and then use the regression tree and the decision tree (CART) to narrow down the number of them to a reasonable scale. After that we introduce the analytic hierarchy process (AHP) model and use Elo rating system to improve the fairness of our system. Questionnaire results verify that the proposed solution not only simplifies the computation but also increases the credibility of the system.

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A Design and Implementation of Intelligent Image Retrieval System using Hybrid Image Metadata (혼합형 이미지 메타데이타를 이용한 지능적 이미지 검색 시스템 설계 및 구현)

  • 홍성용;나연묵
    • Journal of Korea Multimedia Society
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    • v.3 no.3
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    • pp.209-223
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    • 2000
  • As the importance and utilization of multimedia data increases, it becomes necessary to represent and manage multimedia data within database systems. In this paper, we designed and implemented an image retrieval system which support efficient management and intelligent retrieval of image data using concept hierarchy and data mining techniques. We stored the image information intelligently in databases using concept hierarchy. To support intelligent retrievals and efficient web services, our system automatically extracts and stores the user information, the user's query information, and the feature data of images. The proposed system integrates user metadata and image metadata to support various retrieval methods on image data.

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An Efficient Data Mining Algorithm For An Association Rule Discovery (연관성규칙 발견을 위한 데이터마이닝 알고리즘 설계)

  • 이해각
    • Proceedings of the Korea Multimedia Society Conference
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    • 2004.05a
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    • pp.587-591
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    • 2004
  • 수많은 데이터로부터 우리가 이용할 수 있는 의미 있는 연관성 규칙을 찾는 것은 대단히 중요하다. 연관성 규칙은 데이터베이스의 각 트랜잭션을 분석하여 이에 대한 각종 측정치를 수집하여 이루어지는데 대단히 많은 시간과 노력을 요한다. 본 논문에서는 통계적 추론을 이용하여 탐색도중 주어진 조건을 만족하는 항목에 대하여 의사결정을 내려 탐색시간은 단축할 수 있는 알고리즘을 제안한다. 또한 추론에 따른 오류발생을 최소화 할 수 있는 기법을 제시한다.

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