SIEM Alerts: Common Types and Best Practices
When cybercriminals gain access to a network, device, or account, damage control
becomes a race against time. However, the number of apps and accounts that make up
the average tech stack can make attacker behavior a very sharp needle – buried in acres
of hay.
By continuously monitoring and analyzing security events, SIEM technology can detect abnormal patterns or behaviors as they happen – and alert security personnel to the attacker’s precise whereabouts. These events include activities like unauthorized access attempts, unusual network traffic, or system vulnerabilities. Once a potential threat is identified, the SIEM system can generate alerts or notifications to prompt timely investigation and response by security personnel.
However, ensuring your solution is fit for threat detection – without spewing endless SIEM alerts at your security team – is critical. This article will cover the ins and outs of SIEM alerts – what attacks they can help foresee and prevent; and how to best set your SIEM up for success.
By continuously monitoring and analyzing security events, SIEM technology can detect abnormal patterns or behaviors as they happen – and alert security personnel to the attacker’s precise whereabouts. These events include activities like unauthorized access attempts, unusual network traffic, or system vulnerabilities. Once a potential threat is identified, the SIEM system can generate alerts or notifications to prompt timely investigation and response by security personnel.
However, ensuring your solution is fit for threat detection – without spewing endless SIEM alerts at your security team – is critical. This article will cover the ins and outs of SIEM alerts – what attacks they can help foresee and prevent; and how to best set your SIEM up for success.
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What is a SIEM Alert?
SIEM alerts are notifications that inform security professionals about potential security incidents. These alerts are built from the detection, correlation, and aggregation of file metadata and user behavior. For a deeper dive into what SIEM is, our learning resources are a fantastic start. Focusing on the alert process, however, here’s a step-by-step
Event Generation
Almost every file within your on-premises or cloud tenancy is creating a constant flow of
logs. By integrating with these log sources, SIEM technology begins to build an awareness of the real-time processes supporting your firewalls, intrusion detection
systems, antivirus solutions, servers, and other security devices.
Event Collection
Not all logs are created equal – but to establish which are worth taking a closer look at,
SIEM must first collect wide swathes of events from these different sources and
centralize them within its analysis system.
Normalization
Events collected from different sources may use different formats and standards. While
error events indicate a significant problem such as loss of data or loss of functionality,
warning events may just indicate a possible future problem. Alongside this, the sheer
range of file formats and types – from Active Directory to Operating System – demands
the SIEM’s normalization function to standardize these events into a common format.
Event Storage
Normalized events are stored in a secure and centralized database. This allows for
historical analysis, compliance reporting, and forensic investigations.
Detection
Detection involves analyzing events to identify potential security incidents. SIEM systems
use predefined rules, signatures, and behavioral analysis to detect anomalies or patterns
indicative of security threats. Rules might include conditions like multiple failed login
attempts, access from unusual locations, or known malware signatures.
Correlation
Correlation is a crucial step in the SIEM process. It involves analyzing multiple related
events to determine if they collectively represent a security incident. Correlation helps in
identifying complex attack patterns that might go unnoticed when looking at individual
events in isolation.
Aggregation
Aggregation involves combining related events to provide a consolidated view of a
security incident. This step helps in reducing alert fatigue by presenting security
professionals with a more concise and manageable set of alerts.
This process culminates in the generation of an alert. Once a potential security incident is identified through detection, correlation, and aggregation, the SIEM system generates an alert. Alerts include details about the incident, such as the type of threat, affected systems, and the severity of the incident.
This process culminates in the generation of an alert. Once a potential security incident is identified through detection, correlation, and aggregation, the SIEM system generates an alert. Alerts include details about the incident, such as the type of threat, affected systems, and the severity of the incident.
Different Types of Alerts In SIEM
Rather than scrolling through large swathes of data, SIEM alerts aim to provide a focused
and prioritized view of potential threats. Common SIEM alerts examples include:
- Anomalous User Behavior: Security alerts may be triggered when a user exhibits unusual activity, such as multiple unsuccessful login attempts, unauthorized access to resources, or irregular data transfers.
- Monitoring System or Application Errors: SIEM systems meticulously examine logs, promptly alerting on critical errors or failures in systems or applications, revealing potential vulnerabilities or misconfigurations.
- Data Breaches: In response to unauthorized access or the exfiltration of sensitive data, alerts are generated, empowering organizations to react promptly and minimize the resulting impact.
- Compliance Violations: Configurable within SIEM systems, monitoring mechanisms issue alerts in cases of regulatory violations or breaches of internal policies, ensuring adherence to established standards.
When one of these anomalies is discovered, alerts are generated and forwarded to a
centralized Network Operation Center, SRE, or specific DevOps teams for prompt
response. From there, event severity can undergo alert filtering, deduplication, and
analysis – each of which helps to reduce the number of false positives. While IT personnel
have traditionally relied on manual alert triaging, where they assess each issue’s
severity, inbuilt correlation rules now allow SIEM platforms to shoulder more and more of
the weight.
Types of Alert Triggers
Rule-based Triggers are frequently employed in SIEM alerts, relying on predefined
conditions to identify specific events. Security teams leverage these triggers to establish various rules based on diverse aspects, such as known attack patterns, indicators of compromise, or suspicious activities. These rules function as filters, enabling the SIEM system to generate alerts when observed events align with the specified criteria.
Similarly crucial for SIEM, threshold-based triggers involve establishing specific thresholds or limits for events or metrics. When these threshold values exceed or fall below the set parameters, the system generates an alert. This type of trigger proves valuable in detecting abnormal behavior or deviations in patterns.
Anomaly Detection constitutes another vital component of those SIEM alert examples, aiming to identify deviations from anticipated behavior. This process entails analyzing historical data to establish baseline profiles for routine activities. Incoming events are then compared to these baselines, with the system flagging any noteworthy deviations as potential anomalies. Anomaly detection is effective in detecting previously unknown or zero-day attacks, as well as identifying elusive insider threats or unauthorized activities.
Each of these triggers combine to create an adaptive layer of ticketing that fits in nicely with pre-existing ticketing platforms. Some solutions go even further, with AIOps filtering, deduplicating, and normalizing alerts from diverse systems, utilizing AI/ML to identify correlation patterns across the plethora of alerts.
Similarly crucial for SIEM, threshold-based triggers involve establishing specific thresholds or limits for events or metrics. When these threshold values exceed or fall below the set parameters, the system generates an alert. This type of trigger proves valuable in detecting abnormal behavior or deviations in patterns.
Anomaly Detection constitutes another vital component of those SIEM alert examples, aiming to identify deviations from anticipated behavior. This process entails analyzing historical data to establish baseline profiles for routine activities. Incoming events are then compared to these baselines, with the system flagging any noteworthy deviations as potential anomalies. Anomaly detection is effective in detecting previously unknown or zero-day attacks, as well as identifying elusive insider threats or unauthorized activities.
Each of these triggers combine to create an adaptive layer of ticketing that fits in nicely with pre-existing ticketing platforms. Some solutions go even further, with AIOps filtering, deduplicating, and normalizing alerts from diverse systems, utilizing AI/ML to identify correlation patterns across the plethora of alerts.
Best Practices for Managing SIEM Alerts
In hopes of stopping malware before it gets too deep into the network, SIEM wields a
huge scope of alerts, events, and logs – but like a motion-sensor light, sometimes the
alert catches a rat instead of a Remote Access Trojan.
One reason for this ongoing barrage of alerts is a lack of cohesivity between prior security solutions. While IPS, NIDS, and HIDS offer network and endpoint protection respectively, the low quality of alerts issued can rapidly spiral – particularly as integrated security appliances fail to work together, and instead pelt every alert at an overstimulated security team.
SIEM alerts best practices provide a salve to alert noise by consolidating and refining all these alerts – but best practices are essential to keep it fit for purpose, rather than contributing to chronic burnout.
One reason for this ongoing barrage of alerts is a lack of cohesivity between prior security solutions. While IPS, NIDS, and HIDS offer network and endpoint protection respectively, the low quality of alerts issued can rapidly spiral – particularly as integrated security appliances fail to work together, and instead pelt every alert at an overstimulated security team.
SIEM alerts best practices provide a salve to alert noise by consolidating and refining all these alerts – but best practices are essential to keep it fit for purpose, rather than contributing to chronic burnout.
Set Your Own Rules
Rules define an SIEM’s understanding between normal and malicious behavior. A single
alert can have one or more rules, depending on how you define it. While this provides a
strong foundation for catching security events just in time, it’s important to be wary
about creating a large number of customized alerts. Setting up multiple alerts for the
same set of tasks is a surefire way of fogging up security insight.
Check Your Alerts Before Issuing New Ones
Before implementing fresh alert rules, it’s essential to review existing alerts to determine
if there is already a built-in alert serving the same purpose. If none exists, it is imperative
to collect information about the sequence of events that will transpire both before and
after the detection of this alert.
Be Precise When Choosing What to Flag
Alert flooding primarily occurs thanks to vagueness or ambiguity in the alert description
fields. Alongside this, selecting the incorrect category or severity can see relatively
mundane issues turn up in high-priority workflows, drastically bogging IT teams down.
The description needs to be as precise as possible, while the category needs to
accurately reflect the security team’s workflows and priorities.
Keep Regulations In Mind
Every organization needs to comply with various local, regional, and federal laws to meet
its cybersecurity obligations. When creating custom alert rules, keep in mind what each
particular piece of regulation is expecting.
Rely on Both Simple and Composite Rules
Basic SIEM rules are designed to identify a specific event type and initiate a predefined
response. For instance, a simple rule may trigger an alert if an email contains an
attached ZIP file. While basic rules are beneficial, advanced composite rules enable the
combination of two or more rules to identify more intricate patterns of behavior. For
instance, a composite rule might trigger an alert if there are seven failed authentication
attempts to the same computer from a single IP address within ten minutes, using
different usernames. Additionally, if a successful login takes place on any computer
within the network and originates from the same IP address, the composite rule can also
trigger an alert.
Test
Once you’ve crafted an alert, conduct multiple test runs to verify its proper functionality. Rigorous testing of custom alerts enables you to refine your correlation rules, ensuring optimal performance and effectiveness.
While a vital part of SIEM best practice, correlation rules are not smart—they don’t assess the history of the events they evaluate. For example, they don’t care if a computer had a virus yesterday; it’s only interested if a system is infected as the rule is executed. Also, correlation rules are evaluated each time a set is executed – the system doesn’t consider any other data to determine whether or not to evaluate a correlation rule.
This is why the two other forms of threat detection are vital:
While a vital part of SIEM best practice, correlation rules are not smart—they don’t assess the history of the events they evaluate. For example, they don’t care if a computer had a virus yesterday; it’s only interested if a system is infected as the rule is executed. Also, correlation rules are evaluated each time a set is executed – the system doesn’t consider any other data to determine whether or not to evaluate a correlation rule.
This is why the two other forms of threat detection are vital:
Set and Tune Thresholds
Threshold-based triggers involve establishing specific thresholds or limits for events or metrics. When these threshold values exceed or fall below the set parameters, the system generates an alert. This type of trigger proves valuable in detecting abnormal
behavior or deviations in patterns.
While some rules can stay the same, thresholds are some of the most important alert forms to regularly tune. Something as simple as an expansion in userbase or employees can lead to waves of unnecessary alerts.
While some rules can stay the same, thresholds are some of the most important alert forms to regularly tune. Something as simple as an expansion in userbase or employees can lead to waves of unnecessary alerts.
Define Your Anomalies
Alongside set rules, behavior models profile a user, app or account based on their
standard behavior. When the model identifies abnormal behavior, it then applies rules to evaluate and then issue the alert. Make sure to set up models with different classes of behavior types – this allows them to produce distinct alert profiles and drastically speeds
up remedial work.
Similar to correlation rules, a solitary model evaluation typically does not prompt an alert. Instead, the system assigns points to each session based on the models applied. When the accumulated points for a session surpass a predefined threshold, the system then triggers an alert. Establishing and defining this risk tolerance for each model is a critical aspect in managing and controlling the volume of alerts generated.
Similar to correlation rules, a solitary model evaluation typically does not prompt an alert. Instead, the system assigns points to each session based on the models applied. When the accumulated points for a session surpass a predefined threshold, the system then triggers an alert. Establishing and defining this risk tolerance for each model is a critical aspect in managing and controlling the volume of alerts generated.
Next-Generation SIEM Alerts
SIEM solutions are expensive and can be difficult to deploy and configure. However, the
success of your SIEM tool is defined by its ability to tightly integrate with your current tech stack.
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endless mismatched alerts, and flip the script on attackers with next-gen capabilities
such as automated threat hunting and AI-driven analytics. Next-gen SIEM alerts take ultra-flexible data sources and transform them into scalable analytics.
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incidents rather than alerts.