Tax season is notoriously most people’s least favorite time of year. For people who complete their own tax returns, the process becomes an agonizing one of looking at small pieces of paper, matching numbers to the lines that ask for information, and comparing various inputs. In essence, doing your taxes makes you a correlation engine.
Now, imagine taking this tedious process and applying it to the terabytes of data that your environment generates daily. Just as people increasingly automate their annual tax preparation, your security incident and event management (SIEM) correlation engine automates the process of parsing and normalizing log data so that you can compare events that different security tools detect.
A correlation engine searches log data from various technologies across systems and networks, aggregating and analyzing data for real-time insights.
What is Log Correlation?
Log correlation is the process of linking related events across different systems to reveal meaningful activity that isn’t obvious when looking at individual logs. Instead of analyzing each log source in isolation, correlation engines look for relationships—shared attributes, sequences of events, patterns over time, or combinations of conditions—that indicate something important is happening.
By comparing normalized or otherwise aligned data from multiple sources, log correlation can detect behaviors such as
- Coordinated attacks
- Anomalous user activity
- System failures
- Policy violations.
It connects the dots between events that might appear harmless on their own but become significant when viewed together. The goal is to reduce noise, uncover true incidents, and give analysts a clearer, more actionable understanding of what’s occurring across their environment.
In short, log correlation turns fragmented, raw event data into insights by recognizing when separate events are actually part of the same story.
What is a Log Correlation Engine?
A log correlation engine automates the process by connecting different isolated events to provide broader visibility into the organization’s security posture.
A log correlation engine performs the following functions:
- Aggregation: Collecting event log data and application logs from across the environment.
- Log analysis: Applying predefined correlation rules to normalized data streams to detect patterns.
Why is Log Correlation Important?
With a correlation engine, organizations can make connections between diverse activities occurring across their IT environments for improved visibility into security and IT operations.
Real-time Security Incident Detection
A correlation engine helps identify the chain of actions underlying a security incident. Detection chains can surface multi-stage attack campaigns by analyzing multiple related events that would otherwise remain fragmented across isolated alerts. This real-time correlation reduces Mean Time to Detect (MTTD) and other cybersecurity metrics.
Prioritize Vulnerabilities
When integrating a log correlation engine with vulnerability management, organizations can connect vulnerability scanner data to potential risk. Security teams can assign a risk score to an asset based on criticality. With this information, the vulnerability management team can focus remediation activities on the most at-risk assets. Meanwhile, the security team can improve alerts for the assets that pose the greatest organizational risk.
Perform Efficient Root Cause Analysis
Security and operational incidents can lead to service outages that impact business operations. Security and IT teams can use a correlation engine for faster root cause analysis by comparing data generated by:
- Applications
- Devices
- Load balancers
- Servers
- Systems
During a root cause investigation, teams can compare different data points more efficiently, ultimately resolving the issue faster and reducing its business impact.
Optimize Security Operations
Security operations centers (SOCs) have become overwhelmed with false alerts. A log correlation engine consolidates low-level, noisy events into high-fidelity contextualized alerts that reduce alert fatigue. Alert correlation enables SOCs to create risk-based alerts that improve key security metrics, like:
- Mean Time to Investigate (MTTI)
- Mean Time to Contain (MTTC)
- Mean Time to Recover (MTTR)
Meet Compliance Requirements
Various regulatory and compliance frameworks mandate organizations to meet stringent log management, monitoring, and auditing requirements. A log correlation engine enables organizations to create compliance-focused dashboards that automate and document security monitoring.
How is Event Correlation Performed?
A correlation engine performs best when raw logs undergo a structured, multi-stage process that normalizes data so that it can provide meaningful insights.
Data Ingestion
To compare and analyze data, the log correlation engine must receive the message from various sources. These inputs, or log ingestion entry points, are the first step in the log processing pipeline.
Some typical sources include:
- Security tools, like Intrusion Detection Systems (IDS) or endpoint security solutions
- Network devices, like firewalls and routers
- Servers, like Windows or Linux
Some typical formats include:
- Syslog: Messages sent over TCP or UDP.
- CEF: Common Event Format typically used for security-related information.
- Raw HTTP: Data received from sources that can send HTTP requests.
Data Parsing
Parsing is the process of identifying and separating log data elements and fields to make the comparisons easier. Data parsing ensures that the model fields have default values, enabling them to manage missing data without causing errors. Since many technologies define or format fields differently, the parsing process enables the next step in the process.
Data Normalization
The normalization process transforms log data values into a common format to ensure consistency across fields. By transforming diverse formats into a single, standardized format, the normalization process enables security and operations teams to compare data across various devices, networks, applications, and users.
Correlation Searches
After normalizing the fields and data, the correlation engine can start to compare these data points based on predefined rules. Since event correlation focuses on building a series of specific event definitions with a designated sequence, the alerts trigger when all conditions are met.
Some typical use cases for event correlation and log analysis include:
- Per field correlation: rules grouped by a specific field, like user name.
- Cross event source correlations: pattern across multiple sources, like comparing login activity between IDS and IP addresses
- Negative event search: Identify issue when technology fails to submit a log.
- Complex correlation: Building a correlation on top of a previous rule for more nuanced alerts.
Graylog Security: Robust Correlation Engine for High-Fidelity Alerts
Built on the Graylog Platform, Graylog Enterprise and Graylog Security give you the features and functionality of log management and SIEM while eliminating the complexity and reducing costs. Graylog monitors all logs as they enter the system then uses defined event and alert rules to take place high value logs in their own index so you can run queries on the events. Events and correlations events are stored in Elasticsearch, enabling additional filtering, aggregation, or complex correlation.
With our easy to deploy and use solution, you get the combined power of centralized log management, data enrichment and normalization, correlation, threat detection, incident investigation, anomaly detection, and reporting.
With Graylog prebuilt content, you don’t have to worry about choosing the server log data you want because we do it for you. Graylog Illuminate content packs automate the visualization, management, and correlation processes for you.
To see how Graylog can help you improve your security program and help you manage APTs more effectively, contact us today.
