Running Suricata analytics on low-power hardware

Suricata is often treated as “just an IDS engine that writes logs.”
In reality, the real complexity begins when you try to analyze traffic, not merely store alerts.

This becomes critical when your environment is not a powerful server, but something closer to reality:

• Celeron or low-end Xeon
• 4–8 GB RAM
• a single multi-purpose server
• or an edge device

This post is about what actually works for Suricata analytics on low-power hardware — and what does not.

Why popular stacks fail on low-end systems

ELK is not lightweight

Shipping Suricata events into Elasticsearch is a common recommendation.
On low-power hardware it quickly breaks down:

• Elasticsearch consumes memory even when idle
• JVM overhead becomes a bottleneck
• disk I/O limits appear early
• latency grows faster than traffic volume

ELK works well on servers.
On weak hardware it often turns analytics into a resource exhaustion problem.

“Out-of-the-box” SIEMs are not designed for edge

Most SIEM platforms assume:

• centralized backends
• stable server-class hardware
• correlation-heavy workflows For edge and SMB deployments this is often excessive and inefficient.

What Suricata analytics really needs

• If we ignore marketing, the requirements are simple:
• predictable CPU usage
• controlled memory consumption
• stable latency
• minimal dependencies
• focus on flows and behavior, not alerts alone Analytics should support operations, not become a separate system that needs to be managed.

Architectural choices that work in practice

Separate ingestion from analysis

Suricata can generate events very fast.
Problems start when ingestion and analysis are tightly coupled.

A practical approach:

• ingestion and normalization as a dedicated layer
• analytics running asynchronously
• UI as a separate consumer This keeps the system stable even under traffic spikes.

In Suri Oculus (https://suri-oculus.com), ingestion is handled by a dedicated C++ service, allowing analytics and visualization to scale independently.

Redis instead of heavy storage

Redis is often dismissed as “just a cache.”
On low-power systems it works very well as:

• an in-memory event buffer
• a fast handoff between ingestion and analytics
• a responsive data source for UI Redis is not long-term storage — and it should not be. It is a stability and performance layer.

C++ where predictability matters

Python is flexible, but on weak CPUs:

• garbage collection
• interpreter overhead
• unpredictable pauses

become visible very quickly.

For ingestion, parsing, and feature extraction, C++ provides:

• predictable latency
• tight memory control
• stable behavior under load This is a core design choice in Suri Oculus, which targets stable operation even on low-end hardware.

Minimal frontend instead of heavy SPAs

Modern SPAs add complexity and resource usage that is rarely justified for traffic analytics.

A minimal frontend built with:

• plain HTML
• lightweight JavaScript
• simple tables and charts

reduces load on both the server and the client — and makes troubleshooting easier.

AI on low-power hardware: what is realistic

Deep learning models are not a good fit for edge systems.
That does not mean AI is useless.

What does work:

• unsupervised anomaly detection
• compact models like Isolation Forest
• carefully selected features
• inference without on-device training Feature engineering matters more than model complexity.

In Suri Oculus, AI analysis is designed as an optional, isolated component, so it does not destabilize the core system.

Key takeaways

Suricata analytics on low-power hardware is absolutely possible, but only if:

• ingestion is decoupled from analysis
• heavy universal stacks are avoided
• performance-critical paths are predictable
• AI is used selectively, not blindly When done right, analytics becomes a useful operational tool, not an infrastructure burden.

Looking forward

Edge and SMB environments are growing:

• branch offices
• distributed networks
• small data centers
• industrial and IoT segments

These environments do not need another SIEM.
They need lightweight, controllable analytics that respect hardware limits.

That is the design space where projects like Suri Oculus
👉 https://suri-oculus.com

are focused today.