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refactor: distribute Acaua as a Docker image instead of a desktop app
Acaua is a collective SOC daemon consuming only Zeek log files, not a
single-user tool needing local hardware like its siblings. Drop the
Tauri desktop shell, installers and self-update entirely; the server
and its web dashboard ship as a container, updated by pulling a new
image tag.

Link against the official prebuilt libduckdb instead of compiling it
from source via the bundled feature, cutting image build time from
15+ minutes to about 3. Fix a real bug found along the way: as PID 1
in a container, the daemon ignored SIGTERM and was always killed
after the stop timeout; it now shuts down gracefully.

Also corrects the Pillar 1 beaconing score: the period estimate had a
harmonic alias (a perfectly periodic 4h series resolved to 600s), and
the old score formula flagged pure random traffic as suspicious
(p95 = 0.839 on Poisson noise). The new formula was validated against
300 samples per scenario.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VAyr8d7HuAdUGB6aA5xmDD
2026-10-31 19:54:45 -03:00

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Acauã

Acauã

Deterministic network threat hunting.
Zeek-first NDR research · local-first · authorized use.

Terminal · Interface · Architecture · Docker · Support · Roadmap · Changelog

English · Português


Status: pre-alpha skeleton. The project structure, documentation, CI and compiling stubs exist. The four mathematical detection pillars, the real Zeek parser, DuckDB storage and the alert engine are under development and are not shipped yet. Nothing on this page should be read as a claim that detection already works.

Acauã is a network threat hunting engine built around deterministic evidence, not an opaque model. The goal is to answer not only "this looks suspicious" but "these exact inputs, with these intermediate values, produced this score".

  • Deterministic by design: fixed-order reductions, documented sentinels and a JSON evidence record for every future alert.
  • Local-first: the analytical store is embedded in the process; no external database and no telemetry by default.
  • Zeek-first: dns.log, ssl.log, conn.log and the protocol logs needed for identity and lateral-movement analysis. NetFlow/IPFIX is a documented degraded mode, never presented as equivalent. Suricata EVE JSON is an optional secondary input.
  • Server daemon and web dashboard: acaua serve runs continuous ingestion, scheduling, the API and the embedded web UI, distributed 100% via Docker.
  • Research stays outside the product: research/ is an isolated Python workspace for scientific validation and fixtures. It is never compiled, packaged or released.

What exists today

Area State
Rust workspace and CLI stubs ✅ compiles
Axum API with health, status, alerts and analyze routes ✅ stub
Embedded web dashboard ✅ UI shell, served by daemon
Documentation and CI/release workflows ✅
Docker multi-arch image and compose deployment ✅
DuckDB analytical store ✅ official prebuilt library linked
Four detection pillars 🚧 under development
Zeek log parser 🚧 stub
Alert evidence and scoring 🚧 stub
Published releases ❌ none yet

The four pillars

All four are under development. The roadmap keeps them as future work, not as implemented features.

  1. Beaconing: periodicity on event timestamps with a Rayleigh/Schuster periodogram, and generalized Lomb–Scargle only where a real y exists.
  2. Persistent sessions: long connections that never produce a short Δt and would otherwise stay invisible to a beaconing detector.
  3. Protocol anomalies: deterministic features over Zeek protocol logs, including identity and lateral-movement signals.
  4. Evidence fusion: bounded, symmetric combination of pillar scores with a reproducible evidence record per alert.

The design decisions behind these pillars, including the six corrections to the original blueprint, are in docs/architecture.md.

Authorized use

Acauã is defensive software for networks you own or are explicitly authorized to monitor. Capturing or analyzing third-party traffic without permission may be illegal. The operator is responsible for scope, privacy and retention.

The daemon binds to 127.0.0.1 by default. The Docker Compose deployment isolates the daemon in an unprivileged, read-only container with dropped capabilities.

Install

Run via Docker Compose (recommended for operators):

cp .env.example .env
docker compose pull && docker compose up -d

Or build and run locally with Docker:

docker compose up -d --build

To compile from source natively (Rust 1.88+ required):

scripts/fetch-libduckdb.sh ~/.cache/acaua/duckdb
export DUCKDB_LIB_DIR=~/.cache/acaua/duckdb LD_LIBRARY_PATH=~/.cache/acaua/duckdb
cargo build --release -p acaua-cli
./target/release/acaua --help

Supported platform matrix for published container images: linux/amd64 and linux/arm64.

Getting started

acaua doctor
acaua serve --bind 127.0.0.1:8787 --data-dir .acaua
acaua analyze /var/log/zeek/current --json
acaua update

analyze currently validates the directory and returns a skeleton report; it does not parse Zeek logs yet. serve exposes the stub API and the embedded web UI. update checks for new versions and guides container image updates.

Development

scripts/fetch-libduckdb.sh ~/.cache/acaua/duckdb
export DUCKDB_LIB_DIR=~/.cache/acaua/duckdb LD_LIBRARY_PATH=~/.cache/acaua/duckdb
cargo test --workspace
cargo clippy --workspace --all-targets
node scripts/check-ui-i18n.mjs
scripts/check.sh --full

See CONTRIBUTING.md and docs/architecture.md.

Known limits

  • The detection engine, Zeek parser and storage layer are stubs.
  • There is no published release image yet.
  • NetFlow/IPFIX is a degraded input mode by design; it cannot replace the Zeek protocol logs for every pillar.
  • The project targets Linux container environments (linux/amd64, linux/arm64).

Where it's going

Summary of the ROADMAP:

Done Workspace, CLI/API stubs, web dashboard, Docker containerization, CI/release workflows
Next Pillar 1 and 2 implementations with deterministic fixtures; real Zeek parser
Later Full Parquet partitioning, TTL pruning, pillar 3 and 4, evidence fusion
Not planned Opaque AI scoring, a required cloud service, Python in the shipped runtime

License

MIT or Apache-2.0, your choice.