As network attacks grow more prevalent and advanced, Snort has become a ubiquitous intrusion detection system (IDS) relied upon by enterprises and SMBs alike. By analyzing over 30 billion events per day, Snort gives security teams heightened network visibility and early warnings of malicious activity.
However, effectively harnessing Snort‘s firehose of security data poses significant operational challenges. With 50-70% of alerts deemed to be false positives, following up on every alert taxes resources and allows true threats to hide within the noise.
This comprehensive guide provides techniques and best practices for streamlining workflows, optimizing configurations, and maximizing Snort‘s capabilities as a frontline security sensor.
Snort Usage and Statistics
Since its open source debut in 1998, Snort has cemented itself as the most popular network IDS tool on the market comprising 35-40% of the industry with approximately 6 million downloads annually. Developed by Cisco, the most current version of Snort (3.0) handles 300K-3M+ packets per second depending on ruleset complexity.
Out of 120+ predefined Snort rules focused on detecting malware, exploits, reconnaissance attempts, and policy violations, a single IPS/IDS appliance can generate 5,000 – 10,000+ alerts daily. The bulk typically characterized as low to medium severity.
According to research, 50-70% of these alerts prove to be false positives upon investigation costing upwards of $1.27 million in wasted SOC analyst budget over five years. Triaging this avalanche of alerts coupled with configuring and updating custom rules leaves little time for proactive threat hunting.
Optimizing Alert Volumes
Balancing alert quantity and quality remains an ongoing battle. While critical threats must trigger notifications, operational inertia emerges if overwhelmed analysts start ignoring or deleting alerts due to fatigue.
Organizations can optimize Snort‘s potential using several key techniques:
Baseline Traffic
Determine average packets/second and connections for target networks. Irregular spikes where traffic deviates +/- 50% from baseline warrant investigating.
Tune Thresholds Strategically
Instead of removing rules entirely, raise thresholds cautiously to suppress noise without losing visibility into advanced attacks spanning days or weeks.
Consider the risks associated with each environment being monitored to guide threshold adjustments. See examples below:
| Snort Rule | Default Threshold | Updated Threshold |
|---|---|---|
| ICMP Network Scan | 5 alerts in a 60 sec window | 15 alerts in 5 minutes |
| SQL Injection Attack | 5 alerts in 60 seconds | 10 alerts in 5 minutes |
Aggregate Low Severity Alerts
Forward alerts to a SIEM platform to aggregate tens of thousands of low/medium alerts into a handful of prioritized incident tickets instead of individual alerts.
Produce Actionable, Concise Alerts
Configure rules to fire high severity alerts only when multiple conditions align. For example, restrict DHCP server overflow alerts to high priority only after repeated attempts from the same IP address.
Snort Alternatives
While Snort still reigns as the most ubiquitous IDS tool, evaluating alternatives helps identify strengths or weaknesses in detection capabilities by comparing alerts triggered across solutions.
Zeek (formerly Bro): Pattern and anomoly detection focused on identifying malware callback patterns. Handles 1Gb/s+ bandwidth.
Suricata: Open source IDS/IPS built for scalability with 10Gb/s+ throughput potential with parallel processing. Rules compatible with Snort.
Darktrace: AI-based network detection focused on modeling normal user behavior and detecting anomalies in real-time through unsupervised machine learning.
Fidelis Network Sensor: Proprietary solution combining IDS, threat intelligence feeds, sandboxing, and full packet capture for analysis.
Splunk: Heavily used machine data analytics platform with App for Splunk to analyze Snort data. Lacks native packet inspection capabilities.
Each option caters to different use cases based on inspection depth requirements, retention policies, and infrastructure constraints. Most organizations use a mix of tools for layered coverage.
Technical Tuning for Snort Rules
Carefully crafted Snort rules optimize the signal-to-noise ratio in alerting. Use the techniques below to tighten rules over time:
Step 1: Start with wide rule parameters
Monitor overall network patterns before narrowing scope. Cast a wide initial net then tighten thresholds once baseline visibility achieved.
Step 2: Limit monitoring to essential ports/services
Reduce potential noise by omitting unneeded internal traffic (HR portal, print servers, etc.) unlikely to be threat vectors.
Step 3: Specify tighter IP ranges
For outbound traffic to known cloud service provider ranges, restrict scope to detect abnormal outbound connections only.
Step 4: Add suppression rules
Pairs a rule to match suspicious traffic with a suppression rules to filter subsequent alerts as noise.
Step 5: Incorporate threat intelligence
Reference 3rd party IP reputation feeds to suppress alerts for traffic from known benign ranges.
Visual Presentation of Snort Data
Text-based log alerts have limits for conveying security intelligence efficiently. Visual dashboards and heatmaps make high level insights and trends more intuitive through interactive graphs, charts, and maps.
Dashboards
Display Snort statistics like top talkers, protocol breakdowns, events over time, alerts by severity, and detections by rule. Maintain different views for various audiences.
Heatmaps
Use data visualization to reinforce mental models about the environment. See below heatmap depicting source countries of attack from Snort data.
Testing Snort Effectiveness
While dashboards provide visibility into detection patterns, ongoing penetration testing is crucial for confirming Snort has adequate coverage to catch threats.
Quarterly Red Team Exercises
Schedule controlled attack simulations to check Snort‘s ability to detect attacks like protocol anomalies, buffer overflow attempts, reconnaissance probes, or malware command and control traffic.
Analyze Snort‘s forensic data like triggered rulesets compared to outputs from endpoint detection solutions. Determine necessary policy adjustments to improve future response.
TCP Handshake Validation
Some attacks send manipulated TCP handshake sequences disguised as benign traffic. Capture network traffic during testing to check if Snort detects fake handshakes like:
TEST → SYN
TEST ← SYN/ACK
TEST → ACK+PSHIf missed, create custom signatures using the flexible Snort rules language.
Conclusion
Snort‘s highly extensible analysis and detection features provide immense value for monitoring business critical networks and systems at scale over prolonged periods. However, managing the ongoing flood of alerts poses challenges demanding a balanced methodology centered on visibility, streamlined workflows, strategic tuning, and continuous testing.
By learning intelligently from alerts instead of simply reacting to them, organizations can optimize their use of Snort to extract meaningful security insights. Testing effectiveness and iterating on detections cultivates more sophisticated understanding over time.
With adequate care, oversight, and feeding, Snort serves as an invaluable security sentinel keeping watch over essential IT assets. Teams skilled in harnessing Snort‘s capabilities gain deeper awareness into their environments and are well-equipped to hunt threats concealed within network traffic.
