Instructor: In this lesson,
we will learn about alert analysis.
Alert analysis is reviewing and interpreting security alerts
to distinguish between genuine threats and benign activity.
Alert analysis concepts
include false positives and false negatives.
False positives occur when a system
incorrectly flags harmless activity as a security threat.
False negatives, on the other hand,
happen when a system fails to detect real security threats.
Let's learn more about false positives
and false negatives as they are associated
with intrusion prevention and detection systems.
First, we have false positives.
A false positive occurs
when an intrusion prevention system or IPS
or an intrusion detection system or IDS
incorrectly flags harmless activity as a security threat.
This can result in unnecessary alerts
and can overwhelm security teams
with investigations into benign activities
that pose no real threat.
Imagine an IPS or IDS that triggers an alert
every time an authorized internal network scan occurs.
This would be similar to a fire alarm going off
when someone cooks bacon for breakfast every morning.
In both cases, while the system is functioning correctly,
it is flagging non-threatening events,
pulling attention away from genuine security risks.
So with an overly sensitive IDS or IPS configuration,
normal business traffic or routine network activity
can be misinterpreted as malicious,
leading to wasted time and resources in investigation.
Managing false positives in IDS and IPS platforms
requires fine tuning the system's detection rules
to minimize noise while still identifying real threats.
For instance, an intrusion detection system or IDS
might block legitimate traffic between trusted endpoints,
like a company's internal server and a client machine
because it mistakenly interprets that activity
as a potential threat.
In this case, the security team may need to create a rule
that allows traffic from the internal server's IP address
to the client machine's IP address,
while maintaining monitoring for any abnormal behavior
outside of this trusted interaction.
This rule might include an allow list entry
that identifies trusted IP addresses or ports
specifying that all traffic
between these sources allowed without raising an alert.
Additionally, the team might adjust the alert thresholds.
So the IDS only flags traffic
if it exceeds specific patterns,
such as an unusually high data transfer rate
or abnormal protocols being used,
which could signal a real threat.
So continuously monitoring and tweaking these rules
ensures the system remains flexible,
capturing actual threats,
while preventing legitimate business activities
from triggering unnecessary alerts.
The impact of false positives can be significant.
Security analysts may spend hours investigating non-issues,
diverting resources from genuine threats.
In high risk environments,
this can lead to delays in addressing actual threats,
giving malicious actors more time
to exploit vulnerabilities.
So ensure that IDS and IPS systems are calibrated correctly
to minimize false positives
by regularly updating detection rules,
customizing alert settings
and training security staff
to distinguish between false positives and true positives,
where a true positive is an alert on an actual issue.
Second, we have false negatives.
A false negative occurs
when an IPS or IDS system fails to detect
an actual security threat,
allowing malicious activity to occur undetected.
This is like a fire alarm
failing to sound during a real fire,
leaving the building and its occupants at risk.
False negatives can be extremely dangerous
to an enterprise network.
If an IDS or IPS misses a malware infection
or an ongoing attack,
an attacker could move laterally throughout the network,
steal data, or disrupt operations
without anyone noticing until it is too late.
Overall, false negatives create a false sense of security
as the security team may believe
everything is functioning normally,
when in reality a threat is active and undetected.
Detecting and minimizing false negatives
requires a multi-layered approach,
combining different detection techniques,
such as signature-based detection,
behavioral analysis, and anomaly detection.
Signature based detection compares network activity
to known threat patterns,
but it can miss new or sophisticated attacks.
Behavioral analysis looks for deviations
from normal network behavior,
which can highlight suspicious activity
even if no known threat signature is present.
And anomaly detection uses machine learning
to flag irregular patterns
that fall outside predefined baselines,
adding yet another layer of protection.
The combination of these methods and not just one of them
is most likely to detect a false negative.
Additionally, using multiple security tools
in conjunction with the IDS and IPS,
like firewalls, antivirus software and endpoint detection
helps to ensure that even subtle
or new types of attacks are identified.
The use of multiple tools provides overlapping protection
so that if one system fails to detect a threat,
another may catch it.
Finally, cross-referencing data between tools
and integrating threat intelligence feeds
allows for even more comprehensive detection,
reducing the risk of false negatives,
as well as improving overall network security.
So remember, alert analysis involves
reviewing and interpreting security alerts
to identify real threats,
while filtering out benign activity.
Two key concepts in this effort are false positives,
where a detection system incorrectly flags
normal activity as a threat,
and false negatives, where real threats go undetected.
False positives can overwhelm security teams
with unnecessary alerts,
wasting time and resources,
and potentially leading to alert fatigue.
So to reduce false positives,
security teams adjust detection rules,
create allow list for trusted traffic,
and monitor system performance.
False negatives on the other hand,
allow malicious activity to occur undetected,
posing a more serious risk.
So detecting false negatives
requires a multi-layered approach,
combining signature-based detection,
behavioral analysis, and anomaly detection.
Then, by using additional tools,
like firewalls, antivirus software and endpoint detection,
security teams can catch
those subtle or sophisticated attacks
that might otherwise be missed,
reducing the occurrence of both false positives
and false negatives for effective threat detection.