• KIPS Transactions on Computer and Communication Systems
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• v.9 no.7
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• pp.143-148
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• 2020

Recently, web services have been expanded to various areas with the evolution of cloud environment. Whenever a user accesses a web service, the user's log information is stored in the web server. This log information is used as data to analyze the user's web service tendencies and is also used as important data to track the user's system access when a security problem in the system occurs. Currently, most web servers manage user log information in a centralized manner. When user log information is managed in a centralized manner, it is simple in the side of operation, but has a disadvantage of being very vulnerable to external malicious attacks. In the case of centralized management, user log information stored in the web server can be arbitrarily manipulated by external attacks, and in severe cases, the manipulated information can be leaked. In this case, it not only decreases the trust of the web service, but also makes it difficult to trace the source and cause of the attack on the web server. In order to solve these problems, this paper proposes a new method of managing user log information in a cloud environment by applying blockchain technology as an alternative to the existing centralized log management method. The proposed method can manage log information safely from external attacks because user log information is distributed and stored in blockchain on a private network with cloud environment.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.416-427
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• 2005

Web navigation mining is a method of discovering Web navigation patterns by analyzing the Web access log data. However, it is admitted that the log data contains noisy information that leads to the incorrect recognition of user navigation path on the Web's hyperlink structure. As a result, previous Web navigation mining systems that exploited solely the log data have not shown good performance in discovering correct Web navigation patterns efficiently, mainly due to the complex pre-processing procedure. To resolve this problem, this paper proposes a technique of amalgamating the Web's hyperlink structure information with the Web access log data to discover navigation patterns correctly and efficiently. Our implemented Web navigation mining system called SPMiner produces a WebTree from the hyperlink structure of a Web site that is used trl eliminate the possible noises in the Web log data caused by the user's abnormal navigational activities. SPMiner remarkably reduces the pre-processing overhead by using the structure of the Web, and as a result, it could analyze the user's search patterns efficiently.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.6
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• pp.135-144
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• 2009

This paper discusses an issue of serious security risks at web log which contains private information, and suggests solutions to protect them. These days privacy is core information to produce value-added in information society. Its scope and type is expanded and is more important along with the growth of information society. Web log is a privacy information file enacted as law in South Korea. Web log is not protected properly in spite of that has private information It just is treated as residual product of web services. Many malicious people could gain private information in web log. This problem is occurred by no classified data and improper development of web application. This paper suggests the technical solutions which control data in development phase and minimizes that the private information stored in web log, and applies in operation environment. It is very efficient method to protect private information and to observe the law.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.21-26
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• 2006

In this paper, we propose a customer personalized system that presents the web pages to users which are customized to their individuality. It analyzes the action of users who visit the shopping mall, and preferentially supplies the necessary information to them. When they actually buy some items, it forecasts the user's access pattern to web site and their following purchasable items and improves their web page on the bases of their individuality. It reasons the relation among the web documents and among the items by using the log data of web server and the purchase information of DB. For reasoning, it employs Apriori algorithm, which is a method that searches the association rule. It reasons the web pages by considering the user's access pattern and time by using the web log and reasons the user's purchase pattern by using the purchase information of DB. On the basis of the relation among them, it appends the related web pages to link of user's web pages and displays the inferred goods on user's web pages.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.29-32
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• 2006

In this paper, we propose a customer personalized system that presents the web pages to users which are customized to their individuality. It analyzes the action of users who visit the shopping mall, and preferentially supplies the necessary information to them. When they actually buy some items, it forecasts the users' access pattern to web site and their following purchasable items and improves their web pare on the bases of their individuality. It reasons the relation among the web documents and among the items by using the log data of web server and the purchase information of DB. For reasoning it employs Rough Set, which is a method that searches the association rule and offers most suitable cases by reduces cases. It reasons the web pages by considering the users' access pattern and time by using the web log and reasons the users' purchase pattern by using the purchase information of DB. On the basis of the relation among them, it appends the related web pages to link of users' web pages and displays the inferred goods on users' web pages.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.391-404
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• 2019

Customers' fixed characteristics have often been used to predict customer behavior. It has recently become possible to track customer web logs as customer activities move from offline to online. It has become possible to collect large amounts of web log data; however, the researchers only focused on organizing the log data or describing the technical characteristics. In this study, we predict the decision-making time until each customer makes the first reservation, using Airbnb customer data provided by the Kaggle website. This data set includes basic customer information such as gender, age, and web logs. We use various methodologies to find the optimal model and compare prediction errors for cases with web log data and without it. We consider six models such as Lasso, SVM, Random Forest, and XGBoost to explore the effectiveness of the web log data. As a result, we choose Random Forest as our optimal model with a misclassification rate of about 20%. In addition, we confirm that using web log data in our study doubles the prediction accuracy in predicting customer behavior compared to not using it.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.191-197
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• 2005

Application of data mining techniques to the world wide web, referred to as web mining, has been the focus of several recent researches. With the explosive growth of information sources available on the world wide web, it has become increasingly necessary to track and analyze their usage patterns. In this study, we introduce a process of pre-processing and cluster analysis on web log data and suggest a distance measure considering the structural relationships between web contents. Also, we illustrate some real examples of cluster analysis for web log data and look into practical application of web usage mining for eCRM.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.261-271
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• 2001

정보 공유와 비즈니스 수행 등의 매체로서 World Wide Web의 이용이 보편화됨에 따라 다양하고 방대한 데이터를 웹을 통하여 얻을 수 있게 되었으며, 이러한 데이터로부터 유용한 정보를 추출하기 위한 데이터 분석과 활용은 많은 분야에서 중요한 사안으로 인식되고 있다. 본 연구에서는 웹 로그(web log)데이터로부터 정보를 추출하기 위한 과정 및 방안에 대해 살펴보고자 한다. 로그 데이터의 특징과 통계 데이터와의 차이점, 데이터 수집 및 사전 처리 과정, 추출할 수 있는 정보 및 분석 방법 등을 제시하고 로그 데이터 분석 예제를 제시한다.

• Journal of the Korea Computer Industry Society
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• v.5 no.9
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• pp.1063-1068
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• 2004

A log file in web server stores various information of web server. The web server information is good reference information to improve web service quality. Also if web server occurs any fault, log file information is important information to find out occurred error. Currently, many of web server manager that they have wire have disadvantages in mobility. In order to solve above problem, this paper represent web server manager based mobile. Incase web server manager based mobile, it can immediately know recent log information accessing real-rime as also anytime, anywhere