Log analysis-from concept to application

What is log analysis?

Computers, networks, and other IT systems generate audit trail records or log records of system activity. Log analysis is an assessment of these records, helping companies mitigate various risks and meet compliance regulations.

　　How does log analysis work?

Logs are typically created by network devices, applications, operating systems, and programmable/intelligent devices. Contains messages in chronological order and stored in applications such as disks, files, or log collectors.

The analyst needs to ensure that the log covers the full range of messages and is interpreted according to the context. Log elements should be normalized using the same terminology to avoid confusion and to guarantee cohesion. For example, one system might use "warning (warning)" and the other system use "critical" (critical). Ensuring that terminology and data formats are synchronized will help simplify analysis and reduce errors. Normalization also ensures that statistical data and reports from different sources are meaningful and accurate.

After collecting, cleaning, and structuring log data, you can analyze it appropriately to detect patterns and exceptions, such as network * * *.

Cases for log analysis

There are several different uses for log analysis:

Compliance with internal security policies and external regulations, audits

Understanding, responding to data breaches and other security incidents

Troubleshoot a system, computer, or network

Understanding the behavior of users

Forensics during the investigation

If the company wants to obtain a fully compliant certification, log analysis is required. In addition, log analytics can help companies save time when trying to diagnose problems, solve problems, or manage their infrastructure/applications.

　Log analysis software

You can generate logs for almost anything: CDN traffic, database queries, server uptime, errors, and more. Log analysis tools help you extract data from logs and find trends and patterns to guide your business decisions, surveys, and security rules. These tools help you make data-driven decisions that are especially useful for system administrators, network administrators, DEVOPS, security professionals, web developers, and reliability engineers.

　

Best Practices for log analysis

Log analysis is a complex process that includes the following techniques and processes:

1. Pattern detection and Recognition: Filter messages according to the pattern book. Understanding the patterns in the data can aid in detecting exceptions.

2. Normalization: Convert different log elements to the same format.

3. Tagging and categorization: Use keywords to mark log elements and categorize them into multiple classes so that you can filter and adjust how data is displayed.

4. Correlation analysis: Collate logs from different sources and systems and sort meaningful messages related to specific events. Correlation analysis helps identify connections between data that is not visible in a single log, especially because there are multiple security incident records. For example, if you've just experienced network * * *, correlation analysis will put your servers, firewalls, network devices, and other source-generated logs together and look for messages related to that particular * * *. Alerts are associated with this because the data that you collect from the correlation analysis can help you create alerts when certain patterns appear in the log.

5. Intentional neglect: is a machine learning process used to identify and "ignore" useless log entries and detect exceptions. It ignores regular log messages, such as regular system updates, but allows new flags or exception messages to be detected and flagged for investigation. Intentional neglect can also alert you to routine events that should occur but will not occur.

In addition to these techniques and processes, log data should be centralized and structured in a meaningful way so that people can understand them and be interpreted by machine learning systems. By aggregating all the log data from various sources, you can correlate the logs to make it easier to identify relevant trends and patterns. Practice end-to-end logging on all system components, including infrastructure, applications, and end-user clients for a complete overview.

Log analytics is an important feature of monitoring and alerting, judging security policy compliance, auditing and compliance, security incident response, and even forensic investigations. By analyzing log data, organizations can more easily identify potential threats and other problems, find root causes, and initiate rapid response mechanisms to mitigate risk.

Log analysis-from concept to application