Suricata Alert Analysis: Tuning Rules and Promoting Detection to Prevention

This is a follow-up to my last post in which I set up Suricata as an IPS. This article demonstrates how to effectively work with the Suricata engine—specifically, how I analyze its log output, silence unnecessary alerts, and promote specific detection rules to prevention rules.

1. Performance and Rule Management Setup

LibTCMalloc Integration

To enhance Suricata’s performance and stability, I integrate Google’s TCMalloc library to achieve memory usage improvements.

  1. Install the library: apt-get install libtcmalloc-minimal4
  2. Edit the Systemd service (systemctl edit suricata) to preload the library:
# /etc/systemd/system/suricata.service.d/override.conf
[Service]
Environment="LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4"

Rule Update Path Management

I correct the Debian setup where the default rule path conflicts with the update path. I align the configuration to use the dedicated data directory (/var/lib/suricata/rules) for updates, simplifying maintenance.

  1. Edit /etc/suricata/suricata.yaml to point the default rule path:default-rule-path: /var/lib/suricata/rules
  2. I ensure that update.yaml is configured correctly, and remove all initial rules from /etc/suricata/rules to avoid duplicate-rules warnings.

2. Alert Analysis and Rule Tuning (Observability in Practice)

By default, Suricata operates as an IDS (Intrusion Detection System). The critical first step is analyzing the generated alerts (fast.log) to separate actual threats from alert noise.

Initial Alert Frequency Analysis

The following command provides a crucial initial overview by counting unique alert messages and sorting them by frequency. This step is essential to understand the top sources of load and noise.

# awk '{$1=""; $2=""; $3=""}1' fast.log | sed 's_\[\*\*\].*__g' | sed 's_ group [0-9]*__g' | sort | uniq -c | sort -h

# Log Analysis (Excerpt showing frequency)
[..]
    100    GPL RPC portmap listing UDP 111 
    103    SURICATA STREAM 3way handshake excessive different SYN/ACKs 
    176    ET SCAN Suspicious inbound to PostgreSQL port 5432 
    216    ET SCAN Suspicious inbound to mySQL port 3306 
    223    SURICATA UDPv4 invalid checksum 
    236    SURICATA STREAM Last ACK with wrong seq 
    241    GPL ICMP_INFO PING speedera 
    325    ET SCAN Suspicious inbound to MSSQL port 1433 
    ...
  12872    GPL ICMP_INFO PING *NIX 

The Decision to Silence Noise

Alerts like the simple GPL ICMP_INFO PING *NIX often provide no actionable security value and must be disabled to prevent log flooding. I disable logging of ping probes by identifying the specific Signature IDs (SIDs) and adding them to a custom disable.conf file.

Code-Snippet

# /etc/suricata/disable.conf (Excerpt for ICMP PINGs)
# Disabled ping logging
2100366
...
2100480 

3. Promotion to IPS: Hardening the Drop Policy

For the system to transition from passive detection to active prevention (IPS), specific detection rules must be promoted to drop rules.

I promote the ET DROP Dshield Block Listed Source rule, as it targets known hostile IPs, by adding its SID to drop.conf.

# /etc/suricata/drop.conf
# Rules matching SIDs in this file will be converted to drop rules.
2402000 # SID for 'ET DROP Dshield Block Listed Source'

After running suricata-update, the engine confirms the change: -- Dropped 1 rules.

Verifying the Drop (Active Defense Check)

I verify the success of the active drop policy by specifically filtering for dropped packets in the logs.

# Command to output only dropped packets, showing the specific rule that triggered the block:
# awk '/Drop/{...}' fast.log | sort | uniq -c | sort -hr
# Example Output:
   6505 IP dropped due to fail2ban detection
    638 ET DROP Dshield Block Listed Source 

4. Advanced Rule Tuning: Leveraging Variables and Custom Logic

My advice is to use the variables whenever possible. By ensuring that network variables ($HOME_NET, $SMTP_SERVERS, etc.) correctly reflect your environment, you maximize the accuracy of existing rules. This prevents false positives and improves performance.

Enhancing Accuracy with Custom Rules

It’s crucial not just to disable bad rules, but to write custom rules that leverage these network variables for precise defense.

Example: Traffic Segregation Rule

To save resources, I would write a custom rule that only inspects for a vulnerability (e.g., a specific HTTP exploit) when the traffic comes from the external network and is destined for the correct server type.

# Example: Only check for sensitive SQL traffic if it comes from the EXTERNAL net.
# This prevents wasting resources checking internal-to-internal traffic.
# alert tcp $EXTERNAL_NET any -> $SQL_SERVERS 3306 (msg:"ET Custom: External Access to SQL Port"; ...)

This ensures that network resources are conserved by avoiding redundant checks on internal traffic.

5. Modern Analysis: Migrating from Bash to Structured Data

While the Bash pipeline is functional, high-traffic environments quickly overwhelm it. For modern Observability and SecOps analysis, the logs must be processed as structured data.

Migrating to EVE JSON

Suricata can output events in the EVE JSON format, which is ideal for ingestion into systems like Elasticsearch (ELK) or Splunk. This eliminates the slow and unreliable Bash parsing of fast.log.

Configuration Change (in suricata.yaml):

To migrate from the legacy fast.log format, you simply need to enable the EVE logger in your configuration.

# Output module setup in suricata.yaml
outputs:
  - eve-log:
      enabled: yes
      file: eve.json
      # Other settings (e.g., adding flow/metadata fields)

Python for High-Performance Analysis

Instead of relying on slow awk and sed pipelines, I recommend using Python for high-performance log analysis. Python’s built-in json library is optimized to read and aggregate large eve.json files far more efficiently. This elevates the analysis layer of the architecture to a production standard.

Sources / See Also

  1. Suricata Documentation. High-performance AF_PACKET IPS mode configuration and usage. https://docs.suricata.io/en/latest/install/af-packet.html
  2. Suricata Documentation. Working with Suricata-Update (Ruleset Management). https://suricata-update.readthedocs.io/en/latest/update.html
  3. Suricata Documentation. EVE JSON Output for Structured Logging. https://docs.suricata.io/en/latest/output/eve/eve-json-format.html
  4. Google Development. Google Perftools (TCMalloc) Documentation. https://github.com/google/gperftools
  5. Emerging Threats (Proofpoint). Information on the Emerging Threats Open Ruleset. https://www.proofpoint.com/us/security-awareness/blog/emerging-threats
  6. Elastic Stack (ELK) Documentation for Log Analysis. https://www.elastic.co/what-is/elk-stack
  7. Linux Manpage: ethtool (Network Offload Configuration). https://man7.org/linux/man-pages/man8/ethtool.8.html

Automated Defense: Building a Central Log Hub for Fail2ban and External Firewall Integration

A very light-weight and efficient approach for consolidating logs centrally is by using rsyslog. My virtual machines all use rsyslog to forward their logs to a dedicated internal virtual machine, which acts as the central log hub. A fail2ban instance on this hub checks all incoming logs and sends a block command to an external firewall—a process helpful for automated security.

Continue reading Automated Defense: Building a Central Log Hub for Fail2ban and External Firewall Integration