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Journal articles on the topic 'Web-logs'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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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 (May 16, 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 (October 17, 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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4

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 (January 28, 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 (December 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 (August 31, 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 (September 15, 2007): 39–65. http://dx.doi.org/10.1007/s10618-007-0080-z.

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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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9

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 (June 5, 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 activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.

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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 (October 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 infrastructure to store learning logs for analysis. We also describe the method of using query language to retrieve information from a database to construct the data cube. Furthermore, user-friendly operations for manipulating a data cube can retrieve statistical information from the data cube. Although statistical tools for managing Web logs exist, none specifically address the needs of the distance learning instructor. This article uses data cubes and database technology as fundamental analysis tools to satisfy a distance learning instructor's requirements for managing and analyzing learning logs.

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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 (July 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 combines the results of each module in a novel way to capture the different temporal characteristics of the web logs and the mouse movements, as well as the spatial characteristics of the mouse movements. We assess its effectiveness on web bots of two levels of evasiveness: (a) moderate web bots that have a browser fingerprint and (b) advanced web bots that have a browser fingerprint and also exhibit a humanlike behaviour. We show that combining web logs with visitors’ mouse movements is more effective and robust toward detecting advanced web bots that try to evade detection, as opposed to using only one of those approaches.

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12

Jain, Vandita, Tripti Saxena, and Vineet Richhariya. "Analyzing Web Access Logs using Spark with Hadoop." International Journal of Computer Applications 180, no. 1 (December 15, 2017): 47–51. http://dx.doi.org/10.5120/ijca2017915904.

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13

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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14

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

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15

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 (September 2013): 1–37. http://dx.doi.org/10.1145/2508037.2508038.

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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 (December 22, 2014): 1991–2003. http://dx.doi.org/10.1002/sec.1147.

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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 proceed simultaneously, but reduced the performance bottleneck of computing power and storage capacity, thereby saving much time and improving efficiency significantly.

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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 (December 18, 2012): 19–25. http://dx.doi.org/10.5120/9584-4063.

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19

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 (July 1, 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 should be the user’s navigation session file. In this paper the techniques of preprocessing and the method for construction of user’s navigation session file is proposed.

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20

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 (June 29, 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 the right log analyzer in order to perform log analytics economically, efficiently and effectively will give advantages to the organization towards utilizing the primary source of information for identifying the system threats and problems that occur in the system at any time through Visualizing Insights of source using Elastic Stack. These kinds of threats and problems which existed in the system can be identified by analyzing the log file and finding the patterns for possible suspicious behaviour. A case study of UMMAIL’s access logs is proposed to visualise web server logs. The system administrator's concern can then be furnished with an appropriate infographics representation regarding these security threats and problems in the system, which are generated after the log files, are analysed. Based on this signs the administrator can take appropriate actions.

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21

HU, JIA, and NING ZHONG. "WEB FARMING WITH CLICKSTREAM." International Journal of Information Technology & Decision Making 07, no. 02 (June 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 other customer-related data sources. In this paper, we extend the Web log standard to modeling clickstream format and Web mining to Web farming from passively collecting data and analyzing the customer behavior to actively influence the customer's decision making. The proposed model can be developed as a common plugin for most existing commercial websites and portals.

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22

Li, Yun, Yongyao Jiang, Juan Gu, Mingyue Lu, Manzhu Yu, Edward Armstrong, Thomas Huang, et al. "A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch." Applied Sciences 9, no. 6 (March 16, 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, and recommendation. While researches were done in the data discovery domain, collecting and analyzing logs efficiently remains a challenge because (1) the volume of web usage logs continues to grow as long as users access the data; (2) the dynamic volume of logs requires on-demand computing resources for mining tasks; (3) the mining process is compute-intensive and time-intensive. To speed up the mining process, we propose a cloud-based log-mining framework using Apache Spark and Elasticsearch. In addition, a data partition paradigm, logPartitioner, is designed to solve the data imbalance problem in data parallelism. As a proof of concept, oceanographic data search and access logs are chosen to validate performance of the proposed parallel log-mining framework.

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23

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 (April 28, 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 dikembangkan menekankan pada web yang memungkinkan terjadinya komunikasi interaktif antara dosen dan mahasiswa, baik secara individu maupun kelompok, serta dapat menjadi alternatif belajar secara online. Setelah divalidasi oleh ahli desain instruksional, ahli media pembelajaran, dan ahli website designer, didapatkan nilai dengan kriteria “sangat baik” dan memperoleh nilai dengan kriteria “sangat baik” setelah dilakukan uji coba terbatas (N=15) dan uji coba diperluas (N=90) dalam hal kemenarikan desain dan kemudahan penggunaan produk awal dan produk akhir dari WEC yang dikembangkan.Kata kunci: R&D, web enhanced course, fisika dasar I PHYSICS LEARNING BASED ON WEB ENHANCED COURSE: DEVELOPING WEB-LOGS TO SUPPORT PHYSICS I COURSEAbstractThis research aims to (1) develop a Web-blogs by the method of R & D procedures 4D (define, design, develop, disseminate) that can support Web Enhanced Course (WEC) to help to learn Physics 1 material Motion Two-Dimensional student department of physics education UIN Raden Intan Lampung. (2) Know the assessment criteria through product validation from instructional design experts, learning media experts, and web designers expert. Moreover, (3) knowing the students' responses related to the attractiveness of the product and the ease of use of the developed product. Specifications on the WEC developed an emphasis on the web that enables interactive communication between faculty and students, either individually or in groups, and can be an alternative to online learning. After being validated by an instructional design expert, an instructional media expert, and a website designer. A score of "excellent" criteria was obtained and scored with "excellent" criteria after a limited trial (N = 15) and an expanded trial (N = 90) regarding design attractiveness and ease of use of the initial product and end product of the developed WEC.Keywords: R & D, Web Enhanced Course, Physics I

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24

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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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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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 (March 1, 2012): 82–86. http://dx.doi.org/10.7232/iems.2012.11.1.082.

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27

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 (July 15, 2011): 1413–25. http://dx.doi.org/10.3724/sp.j.1001.2011.03820.

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28

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 (March 30, 2016): 83–89. http://dx.doi.org/10.5121/ijdkp.2016.6207.

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29

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

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30

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

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31

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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32

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 (December 20, 2007): 315–27. http://dx.doi.org/10.1108/17440080710848107.

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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 (January 1, 2006): 134–45. http://dx.doi.org/10.1215/1089201x-2005-015.

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34

Diligenti, Michelangelo, Marco Gori, and Marco Maggini. "A unified representation of web logs for mining applications." Information Retrieval 14, no. 3 (December 15, 2010): 215–36. http://dx.doi.org/10.1007/s10791-010-9160-6.

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35

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 (October 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 malicious user behaviour from the regular, high-level log entries generated by web application through its application server. Therefore, the detection of misuse in the web application is achieved without the need of explicit programming or modification of the existing web application. We applied our Intrusion Detection component to a real web-based telemedicine system in order to offer some evaluation measurements. This articles offers an overview of the model, our experiences, and observations.

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36

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 can protect log files effectively, rotate logs automatically, support remote backup and recovery, and support advanced research. The practical application shows that StorLog can not only view logs efficiently and manage and storage system logs conveniently, but also it is simple in function and easy to install and use, therefore, it greatly improves work efficiency and resource utilization of system.

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Sisodia, Dilip Singh. "Augmented Session Similarity Based Framework for Measuring Web User Concern from Web Server Logs." International Journal on Advanced Science, Engineering and Information Technology 7, no. 3 (June 7, 2017): 1007. http://dx.doi.org/10.18517/ijaseit.7.3.1563.

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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 (April 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 when managing the groups. Rather than using statistics packages to evaluate hypotheses, this work presents a methodology of applying existing data and text mining tools to automatically gather learning status and predict performance of learning groups from the contents of discussions and from log records of learning behaviors. Meanwhile, the methodology infers a causal network exists between learning features and learning performance. Knowledge is inferred based on statistics and probability reasoning and social interdependency theory. The causal network can suggest means of enhancing learning performance to teachers. Simultaneously, teachers can use the knowledge of learning groups obtained to manage group learning process on the Web. Experimental results of applying the novel methodology to manage a group learning class organized over the Web and containing 706 students are also presented.

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Román, Pablo E., Robert F. Dell, Juan D. Velásquez, and Pablo S. Loyola. "Identifying user sessions from web server logs with integer programming." Intelligent Data Analysis 18, no. 1 (January 1, 2014): 43–61. http://dx.doi.org/10.3233/ida-130627.

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Richardson, Will. "Web Logs in the English Classroom: More than Just Chat." English Journal 93, no. 1 (September 2003): 39. http://dx.doi.org/10.2307/3650568.

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Mivule, Kato. "Data Swapping for Private Information Sharing of Web Search Logs." Procedia Computer Science 114 (2017): 149–58. http://dx.doi.org/10.1016/j.procs.2017.09.017.

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Narkhede, Sayalee, and Tripti Baraskar. "HMR Log Analyzer: Analyze Web Application Logs Over Hadoop MapReduce." International Journal of UbiComp 4, no. 3 (July 31, 2013): 41–51. http://dx.doi.org/10.5121/iju.2013.4304.

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Motahari-Nezhad, Hamid Reza, Regis Saint-Paul, Fabio Casati, and Boualem Benatallah. "Event correlation for process discovery from web service interaction logs." VLDB Journal 20, no. 3 (September 22, 2010): 417–44. http://dx.doi.org/10.1007/s00778-010-0203-9.

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Asllani, Arben, Lawrence P. Ettkin, and Ashvini Somasundar. "Sharing knowledge with conversational technologies: web logs versus discussion boards." International Journal of Information Technology and Management 7, no. 2 (2008): 217. http://dx.doi.org/10.1504/ijitm.2008.016607.

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Liu, Hongyan, Jun He, Yingqin Gu, Hui Xiong, and Xiaoyong Du. "Detecting and Tracking Topics and Events from Web Search Logs." ACM Transactions on Information Systems 30, no. 4 (November 2012): 1–29. http://dx.doi.org/10.1145/2382438.2382440.

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You, Jingwen, Xiaojuan Wang, Lei Jin, and Yong Zhang. "Anomaly detection in the web logs using user-behaviour networks." International Journal of Web Engineering and Technology 14, no. 2 (2019): 178. http://dx.doi.org/10.1504/ijwet.2019.102871.

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47

Højgaard, Christian, Joachim Sejr, and Yun-Gyung Cheong. "Query Categorization from Web Search Logs Using Machine Learning Algorithms." International Journal of Database Theory and Application 9, no. 9 (September 30, 2016): 139–48. http://dx.doi.org/10.14257/ijdta.2016.9.9.13.

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48

Wu, Yixin, Yuqiang Sun, Cheng Huang, Peng Jia, and Luping Liu. "Session-Based Webshell Detection Using Machine Learning in Web Logs." Security and Communication Networks 2019 (November 22, 2019): 1–11. http://dx.doi.org/10.1155/2019/3093809.

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Attackers upload webshell into a web server to achieve the purpose of stealing data, launching a DDoS attack, modifying files with malicious intentions, etc. Once these objects are accomplished, it will bring huge losses to website managers. With the gradual development of encryption and confusion technology, the most common detection approach using taint analysis and feature matching might become less useful. Instead of applying source file codes, POST contents, or all received traffic, this paper demonstrated an intelligent and efficient framework that employs precise sessions derived from the web logs to detect webshell communication. Features were extracted from the raw sequence data in web logs while a statistical method based on time interval was proposed to identify sessions specifically. Besides, the paper leveraged long short-term memory and hidden Markov model to constitute the framework, respectively. Finally, the framework was evaluated with real data. The experiment shows that the LSTM-based model can achieve a higher accuracy rate of 95.97% with a recall rate of 96.15%, which has a much better performance than the HMM-based model. Moreover, the experiment demonstrated the high efficiency of the proposed approach in terms of the quick detection without source code, especially when it only considers detecting for a period of time, as it takes 98.5% less time than the cited related approach to get the result. As long as the webshell behavior is detected, we can pinpoint the anomaly session and utilize the statistical method to find the webshell file accurately.

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Ardaiz, Oscar, Felix Freitag, and Leandro Navarro. "Estimating the service time of web clients using server logs." ACM SIGCOMM Computer Communication Review 31, no. 2 supplement (April 2001): 108–23. http://dx.doi.org/10.1145/844193.844202.

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Chasteen, Stephanie. "Blogs (Web Logs) for Physics Teachers by Dr. Stephanie Chasteen." Physics Teacher 46, no. 9 (December 2008): 562. http://dx.doi.org/10.1119/1.3023666.

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