• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.337-340
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• 2022

SQL Injection attacks are one of the older attack techniques and are the dominant type of hacking attempts against web services. There have been many attempts to hack SQL injection attacks by exposing data or obtaining privileges. In this paper, we implement a log analysis system that can respond to SQL injection attacks in real time using the open source ELK Stack. did. By providing a visualization of SQL injection attack log data through the implemented system, it is expected that users will be able to easily grasp the degree of attack risk and quickly prepare for attacks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6856-6862
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• 2014

Web usage mining is a method to extract meaningful patterns based on the web users' log data. Most existing patterns of web usage mining, however, do not consider the users' diverse inclination but create general models. Web users' keywords can have a variety of meanings regarding their tendency and background knowledge. This study evaluated the extraction web-user's pattern after collecting and analyzing the web usage information on the users' keywords of interest. Web-user's pattern can supply a web page network with various inclination information based on the users' keywords of interest. In addition, the Web-user's pattern can be used to recommend the most appropriate web pages and the suggested method of this experiment was confirmed to be useful.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.181-190
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• 2009

DBMS access that used high speed internet web service through WAS is increasing. Need application of DB security technology for 3-Tier about DBMS by unspecified majority and access about roundabout way connection and competence control. If do roundabout way connection to DBMS through WAS, DBMS server stores WAS's information that is user who do not store roundabout way connection user's IP information, and connects to verge system. To DBMS in this investigation roundabout way connection through WAS do curie information that know chasing station security thanks recording and Forensic data study. Store session about user and query information that do login through web constructing MetaDB in communication route, and to DBMS server log storing done query information time stamp query because do comparison mapping actuality user discriminate. Apply making Rule after Pattern analysis receiving log by elevation method of security authoritativeness, and develop Module and keep in the data storing place through collection and compression of information. Kept information can minimize false positives of station chase through control of analysis and policy base administration module that utilize intelligence style DBMS security client.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2064-2072
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• 2015

To improve performance, most of Web Services produce and manage User Access Logs. Through the Access Logs, the record provides information about time when the most traffic happens and logs and which resource is mostly used. Then, the log can be used to analyze. However, in case of increasing high traffics of Web Services at the specific time, the performance of Web Service leads to deterioration because the number of processing User Access Logs is increasing rapidly. To solve this problem, we should improve the system performance, or tuning is needed, but it makes a problem cost a lot of money. Also, after it happens, it is not necessary to build such system by spending extra money. Therefore, this paper described the effective Web Service's performance as using improved User Access Log performance. Also, to process the newest data in bulk, this paper includes a method applying some parts of NoSQL using Redis.

• Spatial Information Research
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• v.22 no.4
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• pp.67-76
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• 2014

Recently, through the development of IT and mobile technology, spatial information plays a role of infrastructure of the people life and the national economy. Many kinds of applications including SNS and social commerce is to leverage the spatial information for their services. In the case of domestic, spatial open platform that can provide national spatial data infrastructure services in a stable manner has been released. And many people have been interested to the open platform services. However, the open platform currently has many difficulties to analyze its service status and load in real time, because it does not hold a real-time monitoring system. Therefore, we propose a method that can analyze the real-time service status of the open platform using the analysis of the web server log information. In particular, we propose the results of the analysis as follows: amount of data transferred, network bandwidth, number of visitors, hit count, contents usage, and connection path. We think the results presented in this study is insufficient to understand the perfect service status of the open platform. However, it is expected to be utilized as the basic data for understanding of the service status and for system expansion of the open platform, every year.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.1-11
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• 2018

Recently, the volume of online shopping market continues to be fast-growing, that is important to provide customized service based on customer behavior evaluation analysis. The existing systems only provide analysis data on the profiles and behaviors of the consumers, and there is a limit to the processing in real time due to disk based mining. There are problems of accuracy and system performance problems to apply existing systems to web services that require real-time processing and analysis. Therefore, The system proposed in this paper analyzes the web click log streams generated in real time to calculate the concentration level of specific products and finds interested customers which are likely to purchase the products, and provides and intensive promotions to interested customers. And we verify the efficiency and accuracy of the proposed system.

• The Journal of Bigdata
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• v.2 no.2
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• pp.105-114
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• 2017

Today, many services are supported on the web sites. Analysis of usage patterns of web site visitors is very important to optimize the use and efficiency of the web sites. In this study, analysis of usage patterns and comparative analysis of user groups were conducted by analyzing web access log provided by BPI Challenge 2016. This data provides access logs to the web site in the IT system of a Dutch Employee Insurance Agency (UWV). The customer information, and the click data describing the customers' behavior when using the agency's web site. In this study, we use process mining techniques to analyze the usage patterns of customers and the characteristics of customer groups, and ultimately improve the service quality of customers using web services.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.143-146
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• 2004

With the rapid growth of e-commerce, marketers are able to observe not only purchasing behavior on what and when customers purchased, but also the individual Web usage behavior that affect purchasing. The richness of this information has the potential to provide marketers with an in-depth understanding of customer. Using commonly available Web log data, this paper examines Web usage behaviors at the individual level. By decomposing the buying process into a pattern of visits and purchase conversion at each visit, we can better understand the relationship between Web usage behavior and purchase decision. This allows us to more accurately forecast a shopper's future purchase decision at the site and hence determine the value of individual customers to the siteAccording to our research, not only information seeking behavior but also visiting duration of a customer and participative behavior such as participation in event should be considered as important predicators of purchase decision of customer in a cosmetic internet shopping mall.

• Educational Technology International
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• v.15 no.2
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1501-1508
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• 2015

In this paper, we describe a case study to improve performance through the load testing of multi-tired system for financial accounts before the system opening. The load test was conducted after the data collection tools(Performance Monitor, DB PSSDiag) were installed. By analyzing the collected log, we were able to identify the main sector requiring performance improvements among the presentation tier, web tier, business logic tier and data tier. The ASP.NET server-down on the web tier could be improved by modifying the parameter values in the configuration file. Some server downs occurred on the business logic tier when a large number of users access at the same time, were more difficult to be solved. By analyzing the hang-dump at the server-down time, we were able to find a process that caused the problem. and we had to modify the relevant codes. For major performance improvements of the data-tier, indices of some queries was optimized by using the built-in DBMS query analyzer, after analyzing the log of long-response-time queries. The problems and solutions considered in this case study will be a reference for the performance improvement of a multi-layer system with the similar structure.