• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.446-448
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• 2018

현재 온라인 마케팅 분석을 위해 웹로그 분석시스템을 사용하여 마케팅 결과에 대한 분석을 하고 있지만, 전환에 대한 원인 분석이 사용자의 접속 당시 정보에 의한 데이터로만 분석되고 있어, 본 연구에서는 전환 데이터 분석에 대한 좀 더 현실적으로 분석하기 위한 알고리즘 연구를 제안한다.

• Journal of Information Technology and Architecture
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• v.11 no.1
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• pp.25-32
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• 2014

Web mining techniques are often used to discover useful patterns from data log generated by Web servers for the purpose of web usage analysis. Yet traditional Web mining techniques do not reflect sufficiently sequential properties of Web log data. To address such weakness, we introduce a framework for analyzing Web access log data by using process mining techniques. To illustrate the proposed framework, we show the analysis of Web access log in a campus information system based on the framework and discuss the implication of the analysis result.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.747-751
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• 2017

The log has variable informations that are important and necessary to manage a network when accessed to network servers. These informations are used to reduce a cost and efficient manage a network through the meaningful prediction information extraction from the amount of user access. And, the network manager can instantly monitor the status of CPU, memory, disk usage ratio on network using the SNMP. In this paper, firstly, we have accumulated and analysed the 6 network logs and extracted the informations that used to predict the amount of user access. And then, we experimented the prediction simulation with the time series analysis such as moving average method and exponential smoothing. Secondly, we have simulated the usage ration of CPU, memory, and disk using Xian SNMP simulator and extracted the OID for the time series prediction of CPU, memory, and disk usage ration. And then, we presented the visual result of the variable experiments through the Excel and R programming language.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.95-99
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• 2011

최근 무료 WiFi Zone이 확대되고 있고, Smart Phone으로 무료 WiFi에 접속하여 인터넷으로 접속하여 메신저를 하거나, 메일확인, 정보검색 등을 한다. 하지만 무료 WiFi Zone에서 Smart Phone으로 인터넷을 할 때, 개인정보를 해킹 당 할 수가 있다. 본 논문에서 안드로이드 O,S, Smart Phone에서 무료 WiFi를 이용하여 접속한다. 먼저 메신저와 웹사이트 로그인을 한다. 이때 AirPcap을 이용하여 패킷을 캡처한다. Packet 분석 툴인 WireShark를 사용하여 Packet의 내용을 분석하고, ID, PW와 메신저 대화 내용을 해킹한다. 해킹한 개인정보 ID, 비밀번호를 이용하여 인터넷 사이트에 접속을 하여 관리자 권한을 획득한다. 그리고 Smart Phone에서 WiFi접속 시 공격에 대한 보안대책을 제시한다. 본 연구는 Smart Phone에서 무료 WiFi 접속 때, 보안성 강화연구와 무선 해킹과 방어 기술 발전에 초석이 될 것이다.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1601-1614
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• 2008

The number of web service users has been increased rapidly as existing services are changed into the web-based internet applications. Therefore, it is necessary for us to use web log pre-processing technique to detect attacks on diverse web service transactions and it is also possible to extract web mining information. However, existing mechanisms did not provide efficient pre-processing procedures for a huge volume of web log data. In this paper, we proposed both a field based parsing and a high-speed log indexing mechanism based on the suggested B-tree Index Vector structure for performance enhancement. In experiments, the proposed mechanism provides an efficient web log pre-processing and search functions with a session classification. Therefore it is useful to enhance web attack detection function.

• Convergence Security Journal
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• v.5 no.2
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• pp.43-50
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• 2005

This paper is about the method that chases the Linux kernel backdoor intruder and copes with the kernel backdoor attack. We have a limit to trace the hacker with the current log analysing method because the hacker generally removes the log file and use the forge IP information. I propose the solution to solve the problem with the DeFor system. Through the restoration of the deleted log file, analysis of it and full HDD image, promptly quick response, it is possible to trace hacker spot and reduce hacking damage.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.3
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• pp.431-444
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• 2013

Widespread use of smartphones and wireless LAN accompany a threat called rogue AP. When a user connects to a rogue AP, the rogue AP can mount the man-in-the-middle attack against the user, so it can easily acquire user's private information. Many researches have been conducted on how to detect a various kinds of rogue APs, and in this paper, we are going to propose an algorithm to identify and detect a rogue AP that impersonates a regular AP by showing a regular AP's SSID and connecting to a regular AP. User is deceived easily because the rogue AP's SSID looks the same as that of a regular AP. To detect this type of rogue APs, we use a machine learning algorithm called SVM(Support Vector Machine). Our algorithm detects rogue APs with more than 90% accuracy, and also adjusts automatically detection criteria. We show the performance of our algorithm by experiments.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.321-330
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• 2019

Prevention of corporate confidentiality leakage by insiders in enterprises is an essential task for the survival of enterprises. In order to prevent information leakage by insiders, companies have adopted security solutions, but there is a limit to effectively detect abnormal behavior of insiders with access privileges. In this study, we use the Unsupervised Learning algorithm of the machine learning technique to effectively and efficiently cluster the normal and abnormal access logs of the worker's work screen in the manufacturing information system, which includes the company's product manufacturing history and quality information. We propose an optimal feature selection model for anomaly detection by studying clustering methods.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.723-732
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• 2022

The success of enterprise information system(EIS) is crucial to align with corporate strategies and eventually attain corporate goals. Since one of the factors to information system success is system use, managerial efforts to measure the level of EIS utilization is vital. In this paper, the EIS utilization level is analyzed using system access log data. In particular, process sequence patterns and clustering of similar functions are identified in more detail based on a process mining method, in addition to basic access log statistics. The result of this research can be used to improve existing information system design by finding real IS usage sequences and function clusters.