Raspberry Pi Network Intrusion Detection System

The problem

Home and small-office networks sit in a strange blind spot. They carry real value — credentials, banking sessions, work devices — but almost none of them have any detection capability at all. Commercial NIDS appliances are priced and specified for enterprises, and most "secure router" features are black boxes you cannot tune or inspect.

From 2020 to 2023 I ran a project to close that gap with commodity hardware: a Raspberry Pi running a full network intrusion detection pipeline, feeding a self-hosted SIEM. The constraint was the point — if detection works on a £50 single-board computer, the architecture generalises to any SME that can spare a shelf.

Architecture

The pipeline has three stages, each deliberately boring and replaceable:

• Sensor — Snort3 on the Pi. The Pi sits on a mirrored view of the network traffic and runs Snort3 with a curated ruleset. Snort3's multithreading and Lua-based configuration made it a meaningful upgrade over Snort2 on constrained hardware.
• Shipping — Filebeat. Alerts are written as structured logs and shipped off-device. Keeping storage and analysis off the Pi means the sensor only does the one job it has the CPU for.
• Analysis — Elasticsearch and Kibana. A self-hosted Elastic stack indexes the alerts, and Kibana dashboards handle traffic analysis: top talkers, alert categories over time, signature hit-rates, and the drill-downs needed to decide whether an alert is real.

Earlier iterations were prototyped in Vagrant before committing to hardware, and small pieces of glue — log rotation, enrichment, and capture helpers — were written in Python and C/C++ where performance mattered.

What three years of tuning teaches you

The build took a weekend. The detection quality took years, and that gap is the real lesson of the project.

Default rulesets are unusable as shipped. Out of the box, Snort's community rules produced a wall of alerts dominated by policy noise and protocol anomalies that were normal for this network. The first months were spent disabling, thresholding, and rewriting rules against observed baseline traffic — the same false-positive economics that govern an enterprise SOC, in miniature.

Hardware limits are detection limits. The Pi can inspect a home network's traffic at line rate only if you are selective: sensible capture sizes, pruned preprocessors, and rules scoped to the protocols actually present. Every detection decision was also a performance decision.

Dashboards earn their keep during incidents, not demos. The Kibana views that survived were the ones I actually reached for when something looked wrong — a spike in outbound DNS, a device talking to an address it had never touched. Visualisations that only looked impressive got deleted.

Outcome

The result is continuous, low-cost network visibility: tuned signatures, a searchable alert history, and dashboards that answer questions in minutes. Just as importantly, it is a working reference architecture for SME-scale detection — sensor, shipper, SIEM — built entirely from open-source components.

The project wound down in late 2023, when its successor took over: a cloud-integrated lab that adds machine-learning detection on top of the same telemetry-first philosophy.