Server Logs

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Agglomerative Approach for Identification and Elimination of Web Robots from Web Server Logs to Extract Knowledge about Actual Visitors

Agglomerative Approach for Identification and Elimination of Web Robots from Web Server Logs to Extract Knowledge about Actual Visitors

In this paper we investigate the effectiveness of ensemble-based learners for web robot session identification from web server logs. We also perform multi fold robot session labeling to improve the performance of learner. We conduct a comparative study for various ensemble methods (Bag- ging, Boosting, and Voting) with simple classifiers in perspective of classification. We also evaluate the effectiveness of these classifiers (both ensemble and simple) on five different data sets of va- rying session length. Presently the results of web server log analyzers are not very much reliable because the input log files are highly inflated by sessions of automated web traverse software’s, known as web robots. Presence of web robots access traffic entries in web server log repositories imposes a great challenge to extract any actionable and usable knowledge about browsing beha- vior of actual visitors. So web robots sessions need accurate and fast detection from web server log repositories to extract knowledge about genuine visitors and to produce correct results of log analyzers.
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VISUALIZING WEB SERVER LOGS INSIGHTS WITH ELASTIC STACK– A CASE STUDY OF UMMAIL’S ACCESS LOGS

VISUALIZING WEB SERVER LOGS INSIGHTS WITH ELASTIC STACK– A CASE STUDY OF UMMAIL’S ACCESS LOGS

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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Augmented Session Similarity Based Framework for Measuring Web User Concern from Web Server Logs

Augmented Session Similarity Based Framework for Measuring Web User Concern from Web Server Logs

Abstract— In this paper, an augmented sessions similarity based framework is proposed to measure the web user concern from web server logs. This framework utilized the best user session similarity between any sessions based on the relevance of accessed page in a particular session and syntactic structure of web URLs. The framework is tested using K-medoids clustering algorithms with independent and combined similarity measures. The merits of generated clusters are evaluated by measuring average intra-cluster and inter-cluster distances. The results demonstrated the superiority of combined augmented session dissimilarity metric over the independent dissimilarity measures using augmented session regarding cluster validity measures.
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Proactive montoring of server logs to prevent instant shutdown of the server using ElasticSearch

Proactive montoring of server logs to prevent instant shutdown of the server using ElasticSearch

At present , there are hundreds of millioms of computers, laptops or any electronic devices in not only extravagant but small companies as well generating huge amount of logs. The generation of such huge amounts, we can say, nearly about thousands of billions of information is served by the log files on everyday basis. To predict the business value of any organization it is necessary to store and analyse these log files. Our Oracle databases may not be helpful in storing and analysing such bllions of log events that are being generated. If Oracle databases are taken into consideration then this humungous data must be stored in Oracle clusters which are lowers in performance after scaling and some of the SQL features may be lost.
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A Review of Detection and Prevention of Attacks from HTTP Server logs

A Review of Detection and Prevention of Attacks from HTTP Server logs

A simple example of a DOM-based XSS attack could involve the same setup for the reflected XSS example scenario above. The attacker creates a URL with a malicious script as the “search_term” and solicits it to potential targets. Once a target clicks the URL, their browser loads the site search page and the vulnerable client-side processing scripts. While the “seach_term” is still provided as a query parameter to the site back end for processing, the site itself does not generate the web page with the injected malicious script. Instead, the site’s vulnerable client-side scripts are designed to locally (in the target’s browser) dynamically substitute in the search term value (i.e. the malicious script) in the target’s rendered search page, causing the target’s browser to load and execute the attacker’s script. DOM- based XSS attacks highlight the fact that XSS vulnerabilities aren’t limited to server-side software.
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Detection and Prevention of Attacks from HTTP Server logs

Detection and Prevention of Attacks from HTTP Server logs

Linux operating system. That complete virtualization is implemented on the windows operating system, which serves as the host operating system for Linux system. In the proposed system, server’s logs files are used to analyze the events perform in service provider system or server system. These log files are the activities or events of clients in the network. These log files are analyzed by using rule based analysis in the server in real time. When system finds some malicious activities during log analysis, it generates alerts messages to admin then admin take necessary actions to block the client’s system’s activities by using IDPS’s API.
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WEBSITE LOG ANALYSIS: Case Study of USA Cycling's Website

WEBSITE LOG ANALYSIS: Case Study of USA Cycling's Website

Yeadon (2001) points out that website statistics provide information about usage over time, popularity of certain pages, guide design and uncover navigation problems. These are the most pertinent to web design teams and webmasters wanting to maximize a visitors experience on a website. Yeadon (2001) suggests that following information can be collected from web pages and server logs: Basic-page based counters that display the number of hits on a page; these provide the least information; Intermediate-third party services that gather and report on site usage; the collection agencies logo has to be on page and may distract from visitors experience; and Advanced-computer software packages that collect and analyze server produced log files-Web Trends or Analog.
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Model Survey on Web Usage Mining and Web Log Mining

Model Survey on Web Usage Mining and Web Log Mining

1) Web Server Logs: History of web page requests is maintained as a log file. Web servers are the costly and the most common data source. They collect large volume of information in their log files. These logs contain name, IP, date, and time of the request, the request line exactly came from the client, etc. These data can be bound together as a single text file, or divided into different logs, like access log, referrer log, or error log. However, user specific information is not stored in the server logs [15] 2) Proxy Server Logs: It acts as an intervening level of catching lies between client browser and web servers. Proxy caching is
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													Performance improvement of web server through log files cleaning

1. Performance improvement of web server through log files cleaning

Abstract- Mining the web data is one of the most challenging tasks in the area of data mining and management for research scholars because there is huge heterogeneous, less structured data available on the web. Web server’s logs represent actual usage. Such data have been used for usage-based testing and quality assurance and also for understanding user behavior and guiding user interface design. By analyzing these logs, Web workload was characterized and used to suggest performance enhancements for Web servers. Data preparation techniques and algorithms can be used to process the raw Web server logs, and then mining can be performed to discover users’ visitation patterns for further usability analysis. In this research proposal it is proposed to extract actual user behavior from Web server logs, capture user behavior and also proposed a method which can filter out plenty of irrelevant log values based on the complex prefix of their uniform resource locator. This work will improve data quality of web logs by further filtering out more URL requests comparing with traditional data cleaning methods and analyze the results.
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Generic Log Analyzer Using Hadoop Mapreduce Framework

Generic Log Analyzer Using Hadoop Mapreduce Framework

According to the available scientific papers it seems that the most evolving and developed area of log file analysis is the www industry. Log files of HTTP servers are now-a- days used not only for system load statistic but they offer a very valuable and cheap source of feedback. Providers of web content were the first one who lack more detailed and sophisticated reports based on server logs. They require detecting behavioral patterns, paths, trends etc. Simple statistical methods do not satisfy these needs so an advanced approach must be used. There are over 30 commercially available applications for web log analysis and many more free available on the internet. Regardless of their price, they are disliked by their user and considered too low, inflexible and difficult to maintain. Some log files, especially small and simple, can be also analyzed using common spreadsheet or database programs. In such case, the logs are imported into a worksheet or database and then analyzed using available functions and tools.
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Intelligent Web Server Navigation Usability And Web Usage Data Mining For Improving Users Satisfaction

Intelligent Web Server Navigation Usability And Web Usage Data Mining For Improving Users Satisfaction

Web has recently become a powerful platform for retrieving useful information. It can also be used for discovering knowledge from web based data.As the World Wide Web WWW[1] becomes predominant today, ensuring and building easy-to-use Web based systems is becoming an essential competency for business survival. Because of the vastly un even Web traffic, massive user population, and diverse usage environment, coverage-based testing is insufficient to ensure the quality of Web applications. With the help of simple log cleaner filtered data is valuable and irrelevant, some keep alive link add time stamp into their URL.Mining the web data[2] is one of the most challenging tasks for the data mining and data management scholars because there are huge heterogeneous, less structured data available on the web. Web based server’s logs represent actual usage. Such data have been used for usage-based testing and quality assurance and also for understanding user behavior and guiding user interface design. By analysing these logs[3], Web workload was characterized and used to suggest performance enhancements for Internet Web servers. Data preparation[4] methods and procedures can be used to process the raw Web based server logs. The next is
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Design and Analysis of Multisource Logs Forensic With Lock Technique for Cloud Security Enhancement

Design and Analysis of Multisource Logs Forensic With Lock Technique for Cloud Security Enhancement

The logs used for investigation are more or less labelled into csp facet and patron facet logs. The cloud logs are divided into 3 varieties of logs meta-information server, administrative, and not unusual errors. The CSP-side logs comprise server, audit and occasion logs. Administrative server logs incorporate administrative and safety logs. Likewise there are tracers, unload logs, and common mistakes log files. client aspect logs comprise all syslog’s from AIX, home windows, Linux, and Solaris patron. For brevity, we've used the aforementioned logs to extract the document device state at special intervals of time. An instance of the layout of cloud-aspect logs is shown in table 3, that's founded on logs used by Salesforce [20]. There are other cloud storage services, consisting of SkyDrive, and Dropbox.15. Experimentation conducted on google pressure, created a digital machine for gaining access to this sevice. The digital device became created using VMware computing device model 10. The virtual device was created on a windows platform machine with 30GB potential. The process screen of Sysinternals software became used to gather records related to all modes of modifications that occurred at the same time as
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User Behavior and Devices Analysis of a Large scale Wireless Local Area Network Based on DHCP

User Behavior and Devices Analysis of a Large scale Wireless Local Area Network Based on DHCP

Abstract. The Dynamic Host Configuration Protocol (DHCP) is used to effectively allocate IP addresses on a local area network. Despite the widespread usage of DHCP, few studies related to users exist on DHCP server logs, and even less is known about the user behavior and devices of a large-scale wireless local area network (WLAN). In this paper, we propose a methodology to feasibly analyse mobility and concentration of users and generally analyse the temporal distribution and the providers’ distribution of wireless devices. The method is not involved with user privacy and safety, and fully explores available DHCP metrics and logs. Our results show that individual users are more likely to concentrate in labs and classrooms on weekdays while they prefer to concentrate in their dormitory and library on the weekends. The online time of 80% of wireless devices is less than 10 hours and the provider Apple takes nearly 50% of all wireless devices according to the data set of T University. We except similar benefits to arise for analyzing large-scale WLAN and optimize DHCP.
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Foreseeing Client Conduct through Sessions Utilizing the Web Log Mining

Foreseeing Client Conduct through Sessions Utilizing the Web Log Mining

2) Intermediary Server Logs: It goes about as an interceding level of getting lies between customer program and web servers. Intermediary storing is utilized to diminish the stacking time of a website page and in addition the decrease arrange movement at the server and customer side. The real HTTP ask from different customers to various web servers are followed by the intermediary server [9]. The intermediary server log is utilized as an information hotspot for perusing conduct portrayal of a gathering of unapproved clients sharing a typical intermediary server.
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Analyzing Users Behavior from Web Access Logs using Automated Log Analyzer Tool

Analyzing Users Behavior from Web Access Logs using Automated Log Analyzer Tool

Web server logs stores click stream data which can be useful for mining purposes [5]. They are plain text (ASCII) files which contain information about User Name, IP Address, Time Stamp, Access Request, URL that Referred, error codes (if any) etc. and generally reside in the web servers. Traditionally there are four types of server logs: Transfer Log, Agent Log, Error Log and Referrer Log [6]. The Transfer and the Agent Log are said to be standard whereas the error and referrer log are considered optional as they may not be turned on. Every log entry records the traversal from one page to another, storing user IP number and all the related information [3].
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Geolocalization Techniques using Log Files in Android

Geolocalization Techniques using Log Files in Android

Android uses a specific logging facility and it is quite unknown. Unfortunately enable by default. Android has 3 or 4 different logs depends on the phone model. These logs are not plain text files like in Unix or Linux. These logs are in fact circular memory buffers. It means they have very unique size. These logs are handles by Character device. Files like character device drivers in linux and these drivers are not easy to handle. There is a built in tool in every single android phone. Which will help us to manage or to read these logs. Log is a logging class that you can utilize in your code to print out messages to the LogCat. Common logging methods include
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An economic feasibility of sawing small sized radiata pine logs

An economic feasibility of sawing small sized radiata pine logs

The data on product yields and grades were obtained by sawing 351 sample logs on a standard breast bench. These results were then modified so as to simulate the yields and grades likely to be obtained using an Ecologiser sawmill. Some 68 of the sample logs came from whole trees removed in the course of a normal first thinning at age 19 years. Another 283 were obtained from the residues (tops) remain­ ing after a late first thinning at age 24 years. Four stud sizes were sawn (90 x 45, 90 x 35, 70 x 45 and 70 x 35 mm) and all the resulting timber was dried in a high temperature kiln and then graded both visually and using a mechanical stress grader.
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MATCHING OF PROCESS DATA AND OPERATIONAL DATA FOR A DEEP BUSINESS ANALYSIS: CHECKING THE SUITABILITY OF PROCESS MANAGEMENT TOOLS AND DATABASE MANAGEMENT TOOLS

MATCHING OF PROCESS DATA AND OPERATIONAL DATA FOR A DEEP BUSINESS ANALYSIS: CHECKING THE SUITABILITY OF PROCESS MANAGEMENT TOOLS AND DATABASE MANAGEMENT TOOLS

The table shows the audit data and operational data. The process logs contain the specific information about the specific process and the event logs contain information of the operational data. As discussed above the both if treated separately then pure and unadulterated information can not be attained. So these both logs (process log and event logs) ought to be considered and combined as these are combined in the table. This amalgamation leads us to much more accurate results because now we got the whole picture.
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Termite resistance and wood-penetrability of chemically modified tannin and tannin-copper complexes as wood preservatives

Termite resistance and wood-penetrability of chemically modified tannin and tannin-copper complexes as wood preservatives

Figs.. shape except for the control logs and logs treated with MT, although there were also a few holes made by termites. However, when those logs were cut 5 cm from [r]

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Logs and Exponent notes.doc

Logs and Exponent notes.doc

Problem 1: The exponential function is shown below. Draw the inverse on the same grid.. For example, the inverse of is the logarithm to the base 10 of x , which is written as.. Probl[r]

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