Instructor: In this lesson, we will learn about
SIEM Event Management.
A security information and event management
or SIEM system is a platform that collects,
analyzes, and correlates security event data
from various sources to detect and respond
to potential threats in real time.
SIEM event management is collecting,
analyzing, and responding to security events in a real time
to identify and mitigate threats.
SIEM event management concepts include Event parsing,
Event duplication, as well as the identification
of event false positives and false negatives.
Event parsing refers to the process
of breaking down raw event data
into structured formats for easier analysis.
Event duplication filters out repeated alerts
to reduce noise and enable focus on unique incidents.
Event false positives occur when benign activity
is mistakenly flagged as a threat,
and false negatives occur when actual threats go undetected.
Let's learn more about event parsing, event duplication
as well as the identification
of event false positives and false negatives.
First, we have event parsing.
Event parsing is the process
of breaking down a raw event data into a structured format
that a security information
and event management or a SIEM system can analyze.
Parsing is required because when data is collected
from various sources such as firewalls,
intrusion detection systems, or servers,
it often comes in different formats.
So the SIEM parses this data into a consistent structure,
making it easier to analyze and correlate.
For example, a SIEM may take log data from a firewall
and break it down into standardized fields
like IP address, port number and timestamp.
By doing so, the SIEM can effectively process
and understand the event data across all devices
and systems in a normalized format.
Next, a SIEM accomplishes event parsing
through predefined rules and templates,
which allow it to recognize different types of events
from various sources.
As events enter the SIEM, the system applies these rules
to organize the data
into recognizable and actionable fields.
For instance, if a SIEM receives logs
from both a firewall and an application server,
it will use parsing templates to break down each log type
into relevant fields like Source IP address,
Event Type, and Severity Level.
This structured data can then be used for further analysis,
making event correlation and alerting possible.
In an enterprise network,
the benefit of event parsing
is the ability to quickly normalize data
from various sources,
creating consistency in how events are managed.
This helps security teams reduce noise
and focus on critical issues,
as the structured data allows
for more efficient SIEM filtering,
searching, and correlating of events.
Proper event parsing also ensures
that large volumes of data
can be processed in a meaningful way,
enabling security teams to respond faster to threats.
Second, we have event duplication.
Event duplication refers to the filtering
and elimination of repeated alerts
or events in a SIEM system to avoid unnecessary noise.
In a large enterprise network,
certain events such as repetitive login attempts
or frequent traffic spikes can trigger multiple alerts.
Without duplication management,
these repeated alerts can overwhelm security teams
and obscure true threats.
A SIEM system addresses this
by detecting and removing duplicates,
ensuring that only unique and relevant incidents
are prioritized for investigation.
SIEMs handle event duplication through correlation rules
that identify repeated occurrences
of the same event within a specific timeframe.
For example, if a SIEM receives multiple login failures
from the same user within a short period of time,
it may group D as a single event
or filter out duplicates entirely.
This helps prevent alert fatigue
where security analysts are overwhelmed by too many alerts,
so many that they may miss actual incidents.
So by reducing noise,
the SIEM allows security teams to focus on unique events
that require investigation.
In enterprise environments, managing event duplication
improves the efficiency of the security team.
By eliminating repetitive alerts,
security analysts can focus their efforts on genuine threats
and reduce the time spent investigating duplicate incidents.
This not only increases productivity,
but also ensures that high priority alerts
get the attention they need
without being buried in a flood of redundant data.
Third, we have the identification of false positives
and false negatives.
The identification of false positives and false negatives
ensures accurate threat detection.
A false positive occurs when an event is incorrectly flagged
as a security issue, even though no actual threat exists.
Conversely, a false negative occurs
when a true security threat goes undetected by the system.
SIEMs are designed to minimize both false positives
and false negatives by using correlation rules,
machine learning and fine tuned thresholds.
SIEMs systems reduce false positives
by using and adjusting specific correlation rules
and improving event context,
often with the help of security analysts.
For example, if a SIEM flags in the event
as suspicious because of an unusual login,
but further context reveals that this login was legitimate,
maybe it was a known employee traveling abroad,
then a security analyst can adjust the SIEMs rules
to avoid flagging similar logins in the future.
Similarly, false negatives are addressed
by improving detection methods.
So if a SIEM fails to flag a successful malware download
because the detection rules were too restrictive,
analysts can adjust the rules
to ensure such events are caught in the future.
So the benefit of managing false positives
and false negatives in an enterprise network
is ensuring that security teams
are able to focus on real threats
while minimizing wasted time
on investigating benign incidents.
Reducing false positives helps analysts correlate
on meaningful alerts, improving their efficiency.
At the same time, reducing false negatives
ensures that actual threats are not missed,
maintaining the overall security posture
of the organization.
So remember, a Security Information
and Event Management or SIEM system
is used to collect, analyze, and respond
to security events from various sources in real time.
It helps detect and mitigate potential threats
by processing event data from systems like firewalls,
servers and intrusion detection systems.
Important concepts in SIEM event management
include event parsing, event duplication,
as well as the identification
of event false positives and false negatives.
Event parsing is the process of organizing raw event data
into a structured format for easier analysis,
ensuring consistency and accuracy
across different data sources.
Next, Event Duplication management
filters out repeated alerts, reducing unnecessary noise,
and helping security teams focus on
unique and significant incidents.
And finally, SIEMs also help manage false positives,
which are benign activity mistakenly flagged as a threat
and false negatives,
which are real threats that go undetected.
This allows security teams to focus on real threats
and maintain the organization's
security posture efficiently.