Some of the most detailed insights can be provided by the log file analysis. The insights are about what Googlebot is doing on your site. The foundation of log file analysis is able to verify exactly which URLs have been crawled by search bots. You can import your log file by just dragging and dropping the raw access log file directly into the Log File Analyzer interface and automatically verify the search engine bots. For making a little more accessible to SEO services everywhere, Britney Muller breaks down log file analysis in this week’s Whiteboard Friday.

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Today I’m going to explain you about log file analysis. As it tells you the ins and outs of what Googlebot is doing on your sites, it is very important. Here in this context, I’m going to tell you the primary areas of this; the first one is the types of logs. The second one is how to analyze that data and how to get insights and the last one is how to use that to optimize your pages and your site.

Working of Log Analysis:

Logs are usually created by network devices, applications, operating systems, and programmable smart devices. They comprise of several messages that are chronologically arranged and are stored in a disk, in files, or in an application like a log collector. Analysts need to ensure that the logs consist of complete range of messages and are interpreted according to context. Log elements should be normal by using the same terms or terminology to avoid confusion and to provide cohesiveness. For example, one system might use “warning” while another might use “critical.” Making sure terms and data formats are in sync will help ease analysis and reduce error. Normalization also ensures that statistics and reports are meaningful and accurate from different sources.

Once the log data is collected, cleaned, and structured that they can be properly analyzed to detect patterns and anomalies like network intrusions.

Purpose of Log Analysis:

Log analysis serves several different purposes.

  • To comply with internal security policies, outside regulations and audits.
  • To understand and respond data breaches and other security incidents.
  • To troubleshoot systems, computers, or networks.
  • To understand the behaviors of your users.
  • To conduct forensics at the event of an investigation.

Some organizations are required to conduct log analysis if they want to certified to regulations. However, log analysis also helps companies to save time when they are trying to diagnose problems, resolve issues, or manage their infrastructure or applications.

Log Analysis Best Practices:

Log analysis is a complex process that includes the following technologies and processes.

Normalization:

For converting different log elements such as dates to the same format, it is used.

Classification & Triggering:

It is needed for tagging log elements with keywords. It is also used to categorize them into a number of classes so you can filter and adjust the way you display your data.

Recognition & Patter Detection:

To filter messages based on a pattern book, it is used. Understanding patterns in your data can help you to detect anomalies.

Artificial Ignorance:

Artificial ignorance is a machine learning process to identify and ignore log entries that are not necessary and detect anomalies. Artificial ignorance will ignore routine log messages such as regular system updates but allow for new or unusual messages to detect and flag for investigation. Artificial ignorance can also alert you about routine events that should happen.

Correction Analysis:

This process is often associated with alerting. The data that you gather from correlation analysis can help you to craft alerts when certain patterns in the logs arise. Correction Analysis is used to collate logs from different sources and systems and sort meaningful messages that pertain to a particular event. Correlation analysis helps to discover the connections between data that is not visible in a single log, especially since there are usually multiple records of a security incident.

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