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Journal articles on the topic 'Web-logs'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Reddy, K. Sudheer, G. Partha Saradhi Varma, and I. Ramesh Babu. "Preprocessing the web server logs." ACM SIGSOFT Software Engineering Notes 37, no. 3 (2012): 1–5. http://dx.doi.org/10.1145/2180921.2180940.

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2

M., B., and Haseena Begum. "An Efficient Web Recommender System for Web Logs." International Journal of Computer Applications 152, no. 3 (2016): 9–12. http://dx.doi.org/10.5120/ijca2016911795.

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3

Song, Bo, and Sheng Bo Chen. "Reorganization of Web Site Structure Using Web Logs." Advanced Materials Research 756-759 (September 2013): 1828–34. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1828.

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With the rapid development and evolution of Internet, Web applications play a significant role in people's daily life and daily work. Usually, Web developers design the structure of the web application according to their experiences. But as the evolution of web applications, the existing structure is not enough to meet the needs of the users. This paper proposes an approach to reorganizing the structure of web applications dynamically based on Web logs. Obtained structure of the web application is more reasonable, more convenient to provide services for the users.

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Joshila Grace, L. K., V. Maheswari, and Dhinaharan Nagamalai. "Analysis of Web Logs And Web User In Web Mining." International Journal of Network Security & Its Applications 3, no. 1 (2011): 99–110. http://dx.doi.org/10.5121/ijnsa.2011.3107.

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5

Ingram, Albert L. "Using Web Server Logs in Evaluating Instructional Web Sites." Journal of Educational Technology Systems 28, no. 2 (1999): 137–57. http://dx.doi.org/10.2190/r3ae-ucry-njvr-ly6f.

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6

Manchanda, Mahesh. "Web Usage Mining: Dynamic Methodology to Preprocessing Web Logs." HELIX 8, no. 5 (2018): 3810–15. http://dx.doi.org/10.29042/2018-3810-3815.

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7

Masseglia, F., P. Poncelet, M. Teisseire, and A. Marascu. "Web usage mining: extracting unexpected periods from web logs." Data Mining and Knowledge Discovery 16, no. 1 (2007): 39–65. http://dx.doi.org/10.1007/s10618-007-0080-z.

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Anandha, Jothi, and P. Gayathri. "A Survey on Clustering Algorithms in Web Usage Mining." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 249–51. https://doi.org/10.31142/ijtsrd12742.

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Clustering an information is gathering an data together as indicated by their likeness. There are numerous clustering algorithms are availble here to cluster web logs. In this paper, it mainly focusing on clustering algorithm that is used for clustering web logs. Also this survey is about an algorithm which is used to cluster an web log or web usage data. Anandha Jothi | Gayathri. P "A Survey on: Clustering Algorithms in Web Usage Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL:

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9

Chang, Chih-Kai, Gwo-Dong Chen, and Kou-Liang Ou. "Student Portfolio Analysis by Data Cube Technology for Decision Support of Web-Based Classroom Teacher." Journal of Educational Computing Research 19, no. 3 (1998): 307–28. http://dx.doi.org/10.2190/k6x6-9fmd-yeen-kn42.

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As learners increasingly use Web-based distance learning systems over years, large amounts of learning logs are generated. An instructor needs analysis tools to manage the logs and discover patterns within them to help improve instruction. A variety of analysis tools, including descriptive statistics, statistical inference, prediction, etc., can be utilized to analyze the effects of a teaching strategy from the logs. However, logs of a Web server, as learners' portfolios, cannot satisfy the requirements of these analysis tools. To resolve this problem, a data cube model is proposed as the infr

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10

B. Raut, Aditi. "Web Logs Analysis for Finding Brand Status." IOSR Journal of Computer Engineering 16, no. 4 (2014): 78–85. http://dx.doi.org/10.9790/0661-16467885.

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11

Richardson, Will. "Web Logs in the English Classroom: More Than Just Chat." English Journal 93, no. 1 (2003): 39–43. http://dx.doi.org/10.58680/ej20032609.

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A journalism and literature teacher, Will Richardson has successfully experimented with Web logs. In this article he details the use and appeal of Web logs to enhance classroom discussion and allow for outside involvement in the classroom.

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12

Harika, B., and T. Sudha. "Extraction of Knowledge from Web Server Logs Using Web Usage Mining." Asian Journal of Computer Science and Technology 8, S3 (2019): 12–15. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2113.

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Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activi

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13

Pan, Zulie, Yu Chen, Yuanchao Chen, Yi Shen, and Yang Li. "LogInjector: Detecting Web Application Log Injection Vulnerabilities." Applied Sciences 12, no. 15 (2022): 7681. http://dx.doi.org/10.3390/app12157681.

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Web applications widely use the logging functionality, but improper handling can bring serious security threats. An attacker can trigger the execution of malicious data by writing malicious data to the web application logs and then accessing the view–logs interface, resulting in a vulnerability of the web application log injection. However, detecting this type of vulnerability requires automatic discovery of log-injectable interfaces and view–logs interfaces, which is difficult. In addition, bypasssing the application-specific input-filtering checks to write an effective payload to the log is

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14

Iliou, Christos, Theodoros Kostoulas, Theodora Tsikrika, Vasilis Katos, Stefanos Vrochidis, and Ioannis Kompatsiaris. "Detection of Advanced Web Bots by Combining Web Logs with Mouse Behavioural Biometrics." Digital Threats: Research and Practice 2, no. 3 (2021): 1–26. http://dx.doi.org/10.1145/3447815.

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Web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint, support the main browser functionalities, and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browserlike fingerprint and humanlike behaviour that reduce their detectability. This work proposes a web bot detection framework that comprises two detection modules: (i) a detection module that utilises web logs, and (ii) a detection module that leverages mouse movements. The framework

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15

Wei, Yong Qing, Guang Gang Zhou, Di Xu, and Yu Chen. "Design of the Web Log Analysis System Based on Hadoop." Advanced Materials Research 926-930 (May 2014): 2474–77. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2474.

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As a result of rapid development, the Internet has become an indispensable tool in people's daily life, and web logs have been growing rapidly as well. How to deal with massive logs timely and extract information people need from the logs has become a problem; handling web log by single computer can not meet people's needs any more. Combining cloud computing and Hadoop technology, this paper established a new processing system for the collection of logs and remote parallelization analysis, which not only solved the issues of traditional systems that data handing and collection could not procee

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Asyhari, Moch Yusuf, Juwari Juwari, Abdul Rozaq, and Kelik Sussolaikah. "Pelatihan Web Log Sebagai Media Publikasi Sekolah dan Kegiatan Belajar Mengajar di SDN 1 Ngampel." PaKMas: Jurnal Pengabdian Kepada Masyarakat 2, no. 2 (2022): 340–46. http://dx.doi.org/10.54259/pakmas.v2i2.1267.

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The pandemic makes the utilization and competition of information and communication technology has increased. One of the ways to maintain the existence of schools are publication media of more varied school information and teaching-learning activities media. The purpose of this training program is to provide additional expertise for creating inexpensive and fast publications media and teaching-learning media. The offered solution is the use of web logs from Google which is often known as Blogger. Blogger offers a free and easy web log feature. The training was carried out directly at the Sekol

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17

Qi Wu. "Web Attack Detection Based on Honeypots and Logistic Regression Algorithm." Journal of Electrical Systems 20, no. 3 (2024): 814–22. http://dx.doi.org/10.52783/jes.3013.

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Web security has emerged as one of the most prominent concerns in the realm of cybersecurity. Traditional rule-based methods for detecting web attacks often rely on manual rule definition and pattern matching, leaving them inadequate in accurately identifying new and intricate attack patterns. In the face of these challenges, machine learning techniques have demonstrated potential and advantages. This paper presents a web attack detection method based on honeypots and the logistic regression algorithm. It involves the cleansing, filtering, and analysis of web logs captured by honeypots, follow

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18

Li, Yun, Yongyao Jiang, Juan Gu, et al. "A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch." Applied Sciences 9, no. 6 (2019): 1114. http://dx.doi.org/10.3390/app9061114.

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The volume, variety, and velocity of different data, e.g., simulation data, observation data, and social media data, are growing ever faster, posing grand challenges for data discovery. An increasing trend in data discovery is to mine hidden relationships among users and metadata from the web usage logs to support the data discovery process. Web usage log mining is the process of reconstructing sessions from raw logs and finding interesting patterns or implicit linkages. The mining results play an important role in improving quality of search-related components, e.g., ranking, query suggestion

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19

Jain, Vandita, Tripti Saxena, and Vineet Richhariya. "Analyzing Web Access Logs using Spark with Hadoop." International Journal of Computer Applications 180, no. 1 (2017): 47–51. http://dx.doi.org/10.5120/ijca2017915904.

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20

Wu, Chao, Bin Xu, ShanShan Shi, and Bin Zhao. "Time-activity pattern observatory from mobile web logs." International Journal of Embedded Systems 7, no. 1 (2015): 71. http://dx.doi.org/10.1504/ijes.2015.066144.

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21

Nicholas, David, and Paul Huntington. "Evaluating the use of newspaper web sites logs." International Journal on Media Management 2, no. 2 (2000): 78–88. http://dx.doi.org/10.1080/14241270009389925.

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22

Jiang, Daxin, Jian Pei, and Hang Li. "Mining search and browse logs for web search." ACM Transactions on Intelligent Systems and Technology 4, no. 4 (2013): 1–37. http://dx.doi.org/10.1145/2508037.2508038.

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23

Alsaleh, Mansour, Abdulrahman Alarifi, Abdullah Alqahtani, and AbdulMalik Al-Salman. "Visualizing web server attacks: patterns in PHPIDS logs ‡." Security and Communication Networks 8, no. 11 (2014): 1991–2003. http://dx.doi.org/10.1002/sec.1147.

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24

Muhammad, Harni Yusnidar, and Jasni Mohamad Zain. "VISUALIZING WEB SERVER LOGS INSIGHTS WITH ELASTIC STACK– A CASE STUDY OF UMMAIL’S ACCESS LOGS." MALAYSIAN JOURNAL OF COMPUTING 3, no. 1 (2018): 37. http://dx.doi.org/10.24191/mjoc.v3i1.4882.

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One of the most significant information resources that often overlooked and it is mostly owned by the modern organization today is logs data. Likewise, logs data analytics is practised in many industries for different purposes, including website/system performance improvement, web development, information architecture, web-based campaigns/programs, network traffic monitoring, e-commerce optimization, marketing/advertising, etc. Many tools or approaches are available for this purpose, some are proprietary and some are open source. Studying the nature of these tools in finding the suitable and t

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25

Kumar, Rakesh, Kanwal Garg, and Vinod Kumar. "Extraction of Frequent Patterns from Web Logs using Web Log Mining Techniques." International Journal of Computer Applications 59, no. 10 (2012): 19–25. http://dx.doi.org/10.5120/9584-4063.

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26

Ramanathaiah, Ramakrishnan M., Bhawna Nigam, and M. Niranjanamurthy. "Construction of User’s Navigation Sessions from Web Logs for Web Usage Mining." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4432–37. http://dx.doi.org/10.1166/jctn.2020.9091.

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Web Usage Mining applies fewer techniques in record data to pull out the behavior of users. The knowledge mined from the web log can be utilized in web personalization, Prediction, prefetching, restructuring of web sites etc. It consists of three steps in preprocessing, pattern detection and analysis. Web log information is typically noisy and uncertain and preprocessing is a significant process ahead of mining. The Patterns discovered after applying the mining techniques are dependent on the accuracy of the weblog which in turn depends on the preprocessing phase. The output of preprocessing s

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27

Jose, Jeeva, and Lal P. Sojan. "Analysis of the Temporal Behaviour of Search Engine Crawlers at Web Sites." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 06 (2013): 136–42. https://doi.org/10.5281/zenodo.14602851.

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Web log mining is the extraction of web logs to analyze user behaviour at web sites. In addition to user information, web logs provide immense information about search engine traffic and behaviour. Search engine crawlers are highly automated programs that periodically visit the web site to collect information. The behaviour of search engines could be used in analyzing server load, quality of search engines, dynamics of search engine crawlers, ethics of search engines etc. The time spent by various crawlers is significant in identifying the server load as major proportion of the server load is

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28

HU, JIA, and NING ZHONG. "WEB FARMING WITH CLICKSTREAM." International Journal of Information Technology & Decision Making 07, no. 02 (2008): 291–308. http://dx.doi.org/10.1142/s0219622008002971.

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In a commercial website or portal, Web information fusion is usually from the following two approaches, one is to integrate the Web content, structure, and usage data for surfing behavior analysis; the other is to integrate Web usage data with traditional customer, product, and transaction data for purchasing behavior analysis. In this paper, we propose a unified model based on Web farming technology for collecting clickstream logs in the whole user interaction process. We emphasize that collecting clickstream logs at the application layer will help to seamlessly integrate Web usage data with

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29

Deng, An Yuan. "Research of Log System for IP Network Storage." Advanced Materials Research 181-182 (January 2011): 19–24. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.19.

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As there are many kinds of logs and format, and it is difficult to view and manage the logs, how to view and manage the logs efficiently and effectively has become a crucial issue. At the same time, it is important for the log manager to get the record of user operation and system running, how the log manager view and manage these records proper is of particular importance. However, design and realization of the log management system for IP network storage (StorLog) has solved these problems. StorLog supports kinds of logs, like system logs, web logs, file system logs, storage logs and etc. It

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30

Asyhari, Ardian, and Rahma Diani. "Pembelajaran fisika berbasis web enhanced course: mengembangkan web-logs pembelajaran fisika dasar I." Jurnal Inovasi Teknologi Pendidikan 4, no. 1 (2017): 13. http://dx.doi.org/10.21831/jitp.v4i1.13435.

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Penelitian ini bertujuan untuk mengembangkan Web-blogs dengan metode R&D prosedur 4D (define, design, develop, disseminate) yang dapat mendukung Web Enhanced Course (WEC) agar menunjang pembelajaran Fisika Dasar 1 materi Gerak Dua Dimensi pada mahasiswa Program Studi Pendidikan Fisika UIN Raden Intan Lampung, dan mengetahui kriteria penilaian melalui validasi produk dari ahli desain instruksional, ahli media pembelajaran, dan ahli web designer. Serta mengetahui tanggapan mahasiswa terkait kemenarikan produk dan kemudahan penggunaan dari produk yang dikembangkan. Spesifikasi pada WEC yang d

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31

Husin, Husna Sarirah. "Predicting User Navigation in an Online Newspaper Site Using Association Rules Mining and Markov Model." International Journal of Engineering & Technology 7, no. 4.29 (2018): 40–44. http://dx.doi.org/10.14419/ijet.v7i4.29.21840.

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This paper discusses an approach to predict Web pages from an online newspaper using association rules mining and Markov model decision process. We use a set of Web server logs from an online newspaper, process the logs using Web usage mining methodology, generate transaction files for association mining and predict the web pages using Markov decision model process. We found that users are reading articles from the same section and since majority of users only read one page in a session, it is hard to find associated news articles in a same session. However, the association between section pag

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32

YAMAGUCHI, Yumi, Yuko IKEHATA, Takayuki ITOH, and Yasumasa KAJINAGA. "Visualization of Web access logs using Data Jewelry Box." Journal of the Visualization Society of Japan 22, no. 1Supplement (2002): 111–14. http://dx.doi.org/10.3154/jvs.22.1supplement_111.

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33

R, Vivek, Prasad Mirje, and Sushmitha N. "Recommendation for Web Service Composition by Mining Usage Logs." International Journal of Data Mining & Knowledge Management Process 6, no. 2 (2016): 83–89. http://dx.doi.org/10.5121/ijdkp.2016.6207.

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34

LI, Xiang, Jin-Peng HUAI, Xu-Dong LIU, Hai-Long SUN, and Xian-Yang QU. "Web Service Business Protocol Mining Based on Message Logs." Journal of Software 22, no. 7 (2011): 1413–25. http://dx.doi.org/10.3724/sp.j.1001.2011.03820.

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35

Abdullah, Norhaiza Ya, Husna Sarirah Husin, Herny Ramadhani, and Shanmuga Vivekanada Nadarajan. "Pre-Processing of Query Logs in Web Usage Mining." Industrial Engineering and Management Systems 11, no. 1 (2012): 82–86. http://dx.doi.org/10.7232/iems.2012.11.1.082.

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36

Keyes, Oliver, Bob Rudis, and Jay Jacobs. "R Packages to Aid in Handling Web Access Logs." R Journal 8, no. 1 (2016): 360. http://dx.doi.org/10.32614/rj-2016-026.

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37

Kelly, Tara. "A Resource Guide for Web-Based Physical Activity Logs." Bariatric Surgical Practice and Patient Care 8, no. 2 (2013): 83–84. http://dx.doi.org/10.1089/bari.2013.9986.

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38

Taksa, Isak, Sarah Zelikovitz, and Amanda Spink. "Using web search logs to identify query classification terms." International Journal of Web Information Systems 3, no. 4 (2007): 315–27. http://dx.doi.org/10.1108/17440080710848107.

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39

Bernstein, Mark. "Two open problems in hypertext reading and Web logs." ACM SIGWEB Newsletter 8, no. 1 (1999): 24–26. http://dx.doi.org/10.1145/951413.951419.

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40

Li, Bo, Ying Lin, and Simin Zhang. "Multi-Task Learning for Intrusion Detection on web logs." Journal of Systems Architecture 81 (November 2017): 92–100. http://dx.doi.org/10.1016/j.sysarc.2017.10.011.

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41

Diligenti, Michelangelo, Marco Gori, and Marco Maggini. "A unified representation of web logs for mining applications." Information Retrieval 14, no. 3 (2010): 215–36. http://dx.doi.org/10.1007/s10791-010-9160-6.

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42

Alexanian, J. A. "Publicly Intimate Online: Iranian Web Logs in Southern California." Comparative Studies of South Asia, Africa and the Middle East 26, no. 1 (2006): 134–45. http://dx.doi.org/10.1215/1089201x-2005-015.

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43

GARCÍA ADEVA, JUAN JOSÉ, and JUAN MANUEL PIKATZA ATXA. "WEB MISUSE DETECTION THROUGH TEXT CATEGORISATION OF APPLICATION SERVER LOGS." International Journal on Artificial Intelligence Tools 15, no. 05 (2006): 849–54. http://dx.doi.org/10.1142/s0218213006002989.

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Security in web-based systems that handle confidential information can be considered a particularly sensitive subject that requires assuming some responsibilities about security. Achieving a secure web application involves tackling several issues such encryption of traffic and certain database information, strictly restricted access control, etc. In this work we focus on detecting misuse of the web application in order to gain unauthorised access. We introduce an Intrusion Detection component that by applying Text Categorisation is capable of learning the characteristics of both normal and mal

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44

Murray, James. "AN ASSESSMENT OF FUZZY TEMPORAL EVENT CORRELATION TOWARDS CYBER CRIME INVESTIGATION." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 9, no. 2 (2021): 10–14. http://dx.doi.org/10.55083/irjeas.2021.v09i02006.

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Event logging and event logs play an important role in modern IT systems criminal investigation which is generated when end user with each other in web environment and stored in various logs like firewall log file at side, network log file at gateway and web log file at server side. But log file is not to be over emphasized as a source of information in systems and network management. Whereas conduct efficient investigation and gathering of use full information need to correlate different log file. Task of analysing event log files with the ever-increasing size and complexity of today’s event

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Jin, Jingquan, and Xin Lin. "Web Log Analysis and Security Assessment Method Based on Data Mining." Computational Intelligence and Neuroscience 2022 (August 25, 2022): 1–9. http://dx.doi.org/10.1155/2022/8485014.

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Web content mining describes the classification, clustering, and attribute analysis of a large number of text documents and multimedia files on the web. Special tasks include retrieval of data from the Internet search engine tool W; structured processing and analysis of web data. Today’s blog analysis has security concerns. We do experiments to investigate its safety. Through experiments, we draw the following conclusions: (1) Web log extraction can use efficient data mining algorithms to systematically extract logs from web servers, then determine the main access types or interests of users,

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Rekha, Sundari.M, and Reddy.PVGD Prasad. "PREPROCESSING OF WEB LOG DATA FOR WEB PERSONALIZATION." International Journal of Advances in Engineering & Technology 10, no. 1 (2017): 93–104. https://doi.org/10.5281/zenodo.3958601.

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<em>World Wide Web has its impact on almost every facet of human lives. It is the prevailing and most popularly known information source that is very simply assessable and searchable. Prior to web, useful information can be gathered by referring a document or gathering the data from the expert’s in the related areas, but with the rapid advancements in information technology, web has rigorously changed data seeking behavior of the users. The rapid growth in size and use of World Wide Web with its unique characteristics made web data mining an upcoming and area of demand in the present era

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Chen, Gwo-Dong, Kuo-Liang Ou, and Chin-Yeh Wang. "Use of Group Discussion and Learning Portfolio to Build Knowledge for Managing Web Group Learning." Journal of Educational Computing Research 28, no. 3 (2003): 291–315. http://dx.doi.org/10.2190/3vxr-a5qt-xltp-twpk.

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To monitor and enhance the learning performance of learning groups in a Web learning system, teachers need to know the learning status of the group and determine the key influences affecting group learning outcomes. Teachers can achieve this goal by observing the group discussions and learning behavior from Web logs and aanlyzing the Web log data to obtain the relevant information. However, Web logs are not systematically organized and the discussions are extensive. Consequently, teachers must struggle to extract information from logs and intuitively apply teaching rules based on experience wh

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48

Goddard, Lisa. "Getting to the Source: a Survey of Quantitative Data Sources Available to the Everyday Librarian: Part I: Web Server Log Analysis." Evidence Based Library and Information Practice 2, no. 1 (2007): 48. http://dx.doi.org/10.18438/b8b590.

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This is the first part of a two-part article that provides a survey of data sources which are likely to be immediately available to the typical practitioner who wishes to engage in statistical analysis of collections and services within his or her own library. Part I outlines the data elements which can be extracted from web server logs, and discusses web log analysis tools. Part II looks at logs, reports, and data sources from proxy servers, resource vendors, link resolvers, federated search engines, institutional repositories, electronic reference services, and the integrated library system.

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Goddard, Lisa. "Getting to the Source: a Survey of Quantitative Data Sources Available to the Everyday Librarian: Part II: Data Sources from Specific Library Applications." Evidence Based Library and Information Practice 2, no. 1 (2007): 68. http://dx.doi.org/10.18438/b86c72.

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This is the second part of a two-part article that provides a survey of data sources which are likely to be immediately available to the typical practitioner who wishes to engage in statistical analysis of collections and services within his or her own library. Part I outlines the data elements which can be extracted from web server logs, and discusses web log analysis tools. Part II looks at logs, reports, and data sources from proxy servers, resource vendors, link resolvers, federated search engines, institutional repositories, electronic reference services, and the integrated library system

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50

Akremi, Aymen, Mohamed-Foued Sriti, Hassen Sallay, and Mohsen Rouached. "Ontology-Based Smart Sound Digital Forensics Analysis for Web Services." International Journal of Web Services Research 16, no. 1 (2019): 70–92. http://dx.doi.org/10.4018/ijwsr.2019010104.

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The big data generated by today Web services makes very fastidious and time-consuming the investigators logs management and analysis tasks. This is due partly to the lack of an efficient web service dedicated log data representation. We introduce, in this paper, an extensible standard based semantic ontology representation of Web service log data to identify hidden information and extract eventual scenario of Cyber-attacks in the web logs. The proposed ontology supports the Web service specification and it satisfies the forensics and admissibility requirements. Through a friendly graphical use

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