Snort IDS: How to Avoid False Positives
Tags: snort, ids, cybersecurity, python
Published: 2023-05-09 23:21:04

Snort is one of the most popular and widely used open-source intrusion detection systems (IDS) in the world. In this article, I’ll show some of the most effective ways to avoid false positives in Snort.

## Fine-tune rules

Snort relies on rules to detect and alert on potential intrusions. However, not all rules are created equal, and some may be too general or too specific, leading to false positives. To avoid false positives, it’s important to fine-tune rules to match the specific needs of your network. This can involve adjusting rule thresholds, disabling certain rules that are causing false positives, or creating custom rules that are more tailored to your environment.

* Adjust the threshold of a rule to make it more or less sensitive. For example, if a rule is triggering too many false positives, the threshold can be increased to make it less sensitive.

* Disable a rule that is causing too many false positives. For example, if a rule is triggering alerts for benign activity, it may be better to disable the rule altogether.

* Create a custom rule that is more specific to your environment. For example, if a rule is too general and triggering false positives, a custom rule can be created that is tailored to the specific network traffic on your network.

## Use multiple data sources

Snort can analyze network traffic from a variety of sources, including network packets, logs, and even system calls. By using multiple data sources, administrators can cross-reference data to reduce false positives. For example, if Snort triggers an alert based on network traffic, administrators can check system logs to see if any other activity occurred at the same time that could explain the alert.

* Cross-reference network traffic with system logs to determine if an alert is a false positive. For example, if Snort triggers an alert for a particular IP address, administrators can check system logs to see if that IP address is associated with legitimate traffic or malicious activity.

* Combine network traffic data with endpoint data to identify potential threats. For example, if Snort triggers an alert for suspicious traffic, administrators can check the endpoint associated with that traffic to determine if it is a false positive or a potential security incident.

## Utilize whitelisting

Whitelisting is the practice of explicitly allowing certain traffic to pass through the network while blocking everything else. By creating a whitelist, administrators can reduce the number of false positives triggered by Snort. For example, if a certain type of traffic is known to be safe, administrators can add it to the whitelist so that Snort will not alert on it.

* Allow certain IP addresses, ports, or protocols through the network while blocking everything else. For example, if a certain port is known to be used for legitimate traffic, it can be added to the whitelist so that Snort does not trigger alerts on that traffic.

* Whitelist traffic from trusted sources or known good traffic. For example, if traffic from a particular IP address is known to be safe, it can be added to the whitelist so that Snort does not trigger alerts on that traffic.

## Implement anomaly detection

Anomaly detection is the process of identifying and alerting on activity that is outside of normal behavior. By using anomaly detection, administrators can reduce false positives triggered by rules that are too specific or that may not account for unusual behavior. For example, if Snort triggers an alert for a particular type of traffic that is not usually seen on the network, administrators can investigate the alert further to determine if it is a false positive or a potential security incident.

* Use machine learning to identify activity that is outside of normal behavior. For example, if Snort triggers an alert for network traffic that is not typically seen on the network, machine learning algorithms can be used to determine if the traffic is a false positive or a potential security incident.

* Create baselines for normal network activity and alert on deviations from the baseline. For example, if Snort triggers an alert for a particular type of traffic that is not typical for the network, administrators can investigate the alert further to determine if it is a false positive or a potential security incident.

## Regularly review and update rules

Networks are constantly changing, and Snort rules must be regularly reviewed and updated to ensure they are effective and not causing false positives. This can involve reviewing logs to see which rules are triggering the most alerts, evaluating whether certain rules are still necessary, and adjusting thresholds to reduce false positives.

* Review Snort logs to determine which rules are triggering the most alerts. For example, if a particular rule is causing a high number of false positives, it may need to be adjusted or disabled.

* Evaluate whether certain rules are still necessary or relevant. For example, if a rule is triggering alerts for traffic that is no longer used on the network, it may be unnecessary and can be disabled.

* Adjust thresholds and other settings based on changes in network traffic or security threats. For example, if a new type of malware is discovered, rules may need to be updated or thresholds adjusted to better detect that malware

False positives are a common challenge with Snort and other IDS, but there are several effective ways to reduce them. By fine-tuning rules, using multiple data sources, utilizing whitelisting, implementing anomaly detection, and regularly reviewing and updating rules, administrators can maximize the effectiveness of Snort while minimizing the number of false positives triggered. Ultimately, this will lead to a more secure network and more efficient security operations.