A correlation engine aggregates and correlates content from different sources to uncover an attack.

IT event correlation automates the process of analyzing IT infrastructure events and identifying relationships between them to detect problems and uncover their root cause. Using an event correlation tool can help organizations monitor their systems and applications more effectively while improving their uptime and performance.

Enterprise IT infrastructures generate huge volumes of data in various formats, produced by servers, databases, virtual machines, mobile devices, operating systems, applications, sensors and other network components. An event is any piece of data that provides insight about a state change in that infrastructure, such as a user login. Many of these events are normal and benign but some will signify a problem within the infrastructure. Because a typical enterprise processes thousands of events each day, correlating all of them to determine which are relevant represents a significant challenge for IT teams.

As an answer to this issue, IT event correlation software ingests infrastructure data and uses machine learning to recognize meaningful patterns and relationships. Ultimately, these techniques enable teams to more easily identify and resolve incidents and outages, conduct performance monitoring and help improve the availability and stability of the infrastructure.

In the following sections, we’ll look at how event correlation works, the benefits it offers most organizations, the challenges it addresses and how you can get started using event correlation to better understand your infrastructure data.

What Is IT Event Correlation: Contents

How does IT event correlation work?

IT event correlation relies on automation and software tools called event correlators, which receive a stream of monitoring and event management data automatically generated from across the managed environment. Using AI algorithms, the correlator analyzes these monitoring alerts to correlate events by consolidating them into groups, which are then compared to data about system changes and network topology to identify the cause and ideal solutions of the problems. Consequently, it’s imperative to maintain strong data quality and set definitive correlation rules, particularly when supporting related tasks such as dependency mapping, service mapping and event suppression.

The entire event correlation process generally plays out in the following steps:

• Aggregation: Infrastructure monitoring data is collected from various devices, applications, monitoring tools and trouble ticket systems and fed to the correlator.
• Filtering: Events are filtered by user-defined criteria such as source, timeframe or event level. This step may alternately be performed before aggregation.
• Deduplication: The tool identifies duplicate events triggered by the same issue. Duplication can happen for many reasons (e.g. 100 people receive the same error message, generating100 separate alerts). Often, there is only a single issue to address, despite multiple alerts.
• Normalization: Normalization converts the data to a uniform format so the event correlation tool’s AI algorithm interprets it all the same way, regardless of the source.
• Root cause analysis: The most complex step of the process, event interdependencies are finally analyzed to determine the root cause of the event. (e.g., events on one device are examined to determine its impact on every device in the network).

Once the correlation process is complete, the original volume of events will have been reduced to a handful that require some action. In some event correlation tools, this will trigger a response such as a recommendation of further investigation, escalation or automated remediation, allowing IT administrators to better engage in troubleshooting tasks.

While many organizations correlate different types of events according to their particular IT environments and business needs, there are a few common types of event correlations:

• System events: These events describe anomalous changes in system resources or health. A full disk or high CPU load are both examples of system events.
• Network events: Network events depict the health and performance of switches, routers, ports and other components of the network, as well as network traffic if it falls out of defined thresholds.
• Operating system events: These events are generated by operating systems, such as Windows, Linux, Android and iOS, and describe changes in the interface between hardware and software.
• Database events: These events help analysts and administrators understand how database data is read, stored and updated.
• Application events: Generated by software applications, these events can provide insight into application performance.
• Web server events: These events describe activity in the hardware and software that deliver web page content.
• User events: These indicate infrastructure performance from the perspective of the user and are generated by synthetic monitoring or real-user monitoring systems.

IT event correlation tools make sense of various types of events that will trigger a response or action.

How are events correlated with machine learning?

Event correlation uses a variety of techniques to identify associations between event data and uncover the cause of an issue. The process is driven by machine learning algorithms that excel at identifying patterns and problem causation in massive volumes of data.

These are some of the common event correlation techniques:

• Time-based: This technique examines what happened immediately prior to or during an event to identify relationships in the timing and sequence of events. The user defines a time range or a latency condition for correlation.
• Rule-based: Rule-based correlation compares events to specific variables such as timestamp, transaction type or customer location. New rules must be written for each variable, making this approach impractical for many organizations.
• Pattern-based: This approach combines time- and rule-based techniques to find relationships between events that match a defined pattern. Pattern-based correlation is more efficient than a rule-based approach, but it requires an event correlation tool with integrated machine learning.
• Topology-based: This technique maps events to the topology of affected network devices or applications, allowing users to more easily visualize incidents in the context of their IT environment.
• Domain-based: A domain-based approach ingests monitoring data from individual areas of IT operations such as network performance or web applications and correlates the events. An event correlation tool may also gather data from all domains and perform cross-domain correlation.
• History-based: This technique allows you to learn from historical events by comparing new events to past ones to see if they match. The history-based approach is similar to pattern-based correlation, but history-based correlation can only compare identical events, whereas pattern-based correlation has no such limitations.

Event correlation techniques rely on machine learning algorithms to identify patterns and problems from massive volumes of data

How do you identify patterns in various IT events?

You can easily find patterns and detect anomalies in IT events using an event correlation tool. After you run an initial search of your event data, an analyst can use the tool to group the results into event patterns. Because it surfaces the most common types of events, event pattern analysis is particularly helpful when a search returns a diverse range of events.

Event correlation tools usually include anomaly detection and other pattern identification functions as part of their user interface. Launching a patterns function for anomaly detection, for example, would trigger a secondary search on a subset of the current search results to analyze them for common patterns. The patterns are based on large groups of events to ensure accuracy, listed in order from most prevalent to least prevalent. An event correlation tool lets you save a pattern search as an event type and create an alert that triggers when it detects an anomaly or aberration in the pattern.

What are some of the benefits of IT event correlation?

IT event correlation has many use cases and benefits, including:

• Cybersecurity and real-time malware visibility and detection: IT teams can correlate monitoring logs from antivirus software, firewalls and other security management tools for actionable threat intelligence, which helps identify security breaches and detect threats in real time.
• Reduced IT operational costs: Event correlation automates necessary but time-consuming network management processes, reducing time teams spend trying to understand recurring alerts and providing more time to resolve threats and problems.
• Greater efficiency: Manual event correlation is laborious and time-consuming and requires expertise — factors that make it increasingly more challenging to conduct as infrastructure expands. Conversely, automated tools increase efficiency and make it easy to scale to align with your SLAs and infrastructure.
• Easier compliance: Event correlation facilitates continuous monitoring of all IT infrastructures and allows you to generate reports detailing security threat and regulatory compliance measures.
• Reduced noise: Of the thousands of network events that occur every day, some are more serious than others. Event correlation software can quickly sift through the reams of incidents and events to determine the most critical ones and elevate them as top priorities.

Essentially IT event correlation helps businesses ensure the reliability of their IT infrastructure. Any IT issue can threaten a business’s ability to serve its customers and generate revenue. According to a 2020 survey, 25% of respondents worldwide reported the average hourly downtime cost of their servers was as high as US $400,000. Event correlation helps mitigate these downtime costs by supporting increased infrastructure reliability.

Can you protect IT infrastructure with IT event correlation monitoring?

Event correlation can support network security by analyzing a large set of event data and identifying relationships or patterns that suggest a security threat.

As an example, imagine you notice multiple login attempts in a short amount of time on an account that has been dormant for years. After successfully logging in, the account begins executing suspicious commands. With the help of event correlation, an intrusion detection system could recognize these related events as a potential cyberattack and alert the appropriate team.

An event correlation tool can map and contextualize the data it ingests from infrastructure sources to identify suspicious patterns in real time. Some event correlation tools will also produce correlation reports for common types of attacks, including user account threats, database threats, Windows and Linux threats and ransomware, among others.

Event correlation equips IT teams to better respond to security threats and develop stronger policies to prevent them.

What are some of the biggest challenges to efficient event correlation?

Since the dawn of enterprise computing, event correlation has been an essential practice for identifying and resolving IT problems that can have negative business impacts.

Historically, event correlation was a manageable manual process for IT teams when networks were simpler and predominantly contained on-premises. But today’s dynamic network environments can produce thousands or millions of events in a single day. Keeping up with the volume of events that modern infrastructures generate, let alone parsing them into actionable information, is beyond human capabilities. Event correlation technology can perform this task more quickly and cost effectively while freeing IT teams to focus more on resolving the problems instead of detecting them.

How do you integrate IT event correlation into SIEM?

IT event correlation integrates into security information and event management (SIEM) by taking the incoming logs and correlating and normalizing them to make it easier to identify security issues in your environment. The process requires both the SIEM software and a separate event correlation engine. As such, it's important to consider how each works to understand the benefit of using them together.

Learn about Splunk SIEM for Enterprise Security.

At its most basic level, SIEM collects and aggregates the log data generated throughout an organization’s IT infrastructure. This data comes from network devices, servers, applications, domain controllers and other disparate sources in a variety of formats. Because of its diverse origins, there are few ways to correlate the data to detect trends or patterns, which creates obstacles to determining if an unusual event signals a security threat or just an aberration.

Event correlation takes in all the logs entering your system and converts them into a consistent, readable format. Once logs are normalized, analysts can string together the clues spread among multiple types of logs and detect incidents and security events in real time. Event correlation also brings more clarity to log sources so you can recognize trends in incoming events.

How do you get started with event correlation?

To get started with event correlation, you need to find an event correlation solution that meets your organization’s specific needs. Consider the following when evaluating event correlators:

• User experience: As with any new software, it’s important to consider how easy — or difficult — it will be for users to learn, understand and use. A good event correlator will have a modern interface with intuitive navigation and a management console that integrates with your IT infrastructure. Its native analytics should be easy to set up and understand, and it should also easily integrate with the best third-party analytics systems.
• Features and functionality: It’s critical to know what data sources a data correlator can ingest and in what formats. It’s also important to look at what types of events the tool can correlate (monitoring, observability, changes, etc.) and what steps it takes to process event data (normalization, deduplication, root cause analysis, etc.). The ability to trigger appropriate, corresponding actions (such as automated remediation) is also a desired feature.
• Machine learning and anomaly detection capabilities: While you don’t have to be a data scientist to use an event correlator, it helps to have a basic understanding of machine learning to better inform your purchasing decision. There are essentially two types of machine learning: supervised and unsupervised.
  • Supervised machine learning uses a structured dataset that includes examples with known, specific outcomes to guide the algorithm. The algorithm is told what variables to analyze and given feedback on the accuracy of its predictions. In this way, the algorithm is “trained” using existing data to predict the outcome of new data.
  • Unsupervised machine learning, on the other hand, explores data without any reference to known outcomes. This allows it to identify previously unknown patterns in unstructured data and cluster them according to their similarities. Machine-generated data formats widely vary, ranging from structured syslog data to unstructured multi-line application data, making it essential that a correlator supports both supervised and unsupervised machine learning.

Beyond these criteria, it’s also important to check that any event correlator you’re considering can integrate with other tools and vendor partners you’re currently working with. In addition, it should also help you meet your business's or industry’s compliance requirements, as well as offer robust customer support.

Once you've gotten started, optimize the practice with event correlation best practices.

What is the future of IT event correlation?

The growing complexity of modern infrastructures and a more aggressive, sophisticated threat landscape have combined to make it more challenging than ever for IT teams to detect and resolve performance problems and security incidents. As these factors compound, event correlation will become an increasingly important tool for organizations to make certain their IT services are reliable. To that end, IT event correlation will continue to support and optimize network self-healing.

Event correlation will also need to continue to harness advances in analytics and artificial intelligence to keep pace with this dynamic environment. It will be especially significant in AIOps as organizations seek to process and analyze a flood of alerts in real time before they escalate into outages or network disruptions.

The Bottom Line: Event correlation makes sense of your infrastructure

The clues to performance issues and security threats within your environment are in your event data. But IT systems can generate terabytes’ worth of data each day, making it virtually impossible to determine which events need to be acted upon and which do not. Event correlation is the key to making sense of your alerts and taking faster and more effective corrective action. It can help you better understand your IT environment and ensure it's always serving your customers and your business.

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