Walks through the full flow — passkey registration, passkey login,
new conversation lookup, empty thread, first message, end-to-end
chat, and mobile view — matching the format used by the bug-bounty
and SIEM project demos.
- New Aenebris.ML.Middleware module: Wai middleware that wires the
Aenebris.ML.Engine into the per-request pipeline. Per request:
1. Run extractFeatures (FeatureContext + Request) -> FeatureVector
2. Convert via featureVectorToVector -> VU.Vector Double
3. runEngine eng fv -> DecisionDetails
4. Route by ddDecision:
DecisionHuman -> pass through to inner Application,
optionally attach X-Aenebris-ML-Decision
and X-Aenebris-ML-Score response headers
DecisionBot -> 403 + text/plain block body
DecisionChallenge -> 403 + text/html challenge page
All response builders are operator-overridable via the
MLMiddlewareConfig record. Optional logging callback fires once
per request for structured logs / metrics integration.
- defaultBotResponse, defaultChallengeResponse: sensible defaults
matching the existing WAF middleware pattern (403 + descriptive
body). decisionResponseHeader / scoreResponseHeader are exported
so operators can build custom responses that still surface the
signal headers.
- 12 tests via Network.Wai.Test.runSession covering: pass-through
on Human, 403 on Bot, 403 on Challenge with text/html body,
signal-header attachment toggle, custom response builder override,
logging callback invocation count.
- aenebris.cabal: expose Aenebris.ML.Middleware in the library stanza.
- test/Spec.hs: add modifyTVar' to the Control.Concurrent.STM import
list for the logging-callback test.
- 354 total examples passing, 0 GHC warnings on the new module.
This closes the core Phase 2.5 ML pipeline. The full sequence
(Features -> Model -> Loader -> Inference -> Calibration -> IForest
-> Engine -> Middleware) is now fully implemented and integration-
tested. A hosted Aenebris instance can now be configured with a
trained LightGBM model + optional calibrator + optional IForest
and will block bots, challenge ambiguous traffic, and pass humans
on every HTTP request.
- New Aenebris.ML.Engine module: pure decision pipeline composing
the previously-built Loader, Inference, Calibration, and IForest
modules into a single per-request scoring path.
Engine record holds (Ensemble, Calibrator, Maybe IForest,
EngineConfig); runEngine takes a feature vector and produces
DecisionDetails (Decision, raw proba, calibrated proba, optional
IForest score). The Decision is one of DecisionHuman, DecisionBot,
or DecisionChallenge.
- Implements the escalation-gate semantics per the 2026 research
correction (over the deprecated 0.8/0.2 weighted blend):
calibrated <= humanThreshold -> DecisionHuman
calibrated >= botThreshold -> DecisionBot
otherwise (ambiguous band):
if IForest configured and ifScore >= escalation threshold
-> DecisionBot (escalation)
else if challenges enabled
-> DecisionChallenge
else
-> DecisionHuman (fallthrough)
Defaults: 0.3 / 0.7 thresholds, 0.6 IForest escalation, challenges
on by default.
- 17 tests covering: defaultEngineConfig values, decision boundaries
with NoCalibrator and no IForest (low/mid/high leaf ensembles),
ambiguous-band escalation via low-vs-high anomaly IForests,
ambiguous-band with challenges disabled (fallthrough to Human or
escalation to Bot), Platt calibrator pulling decisions across
thresholds, and DecisionDetails field correctness.
- aenebris.cabal: expose Aenebris.ML.Engine in the library stanza.
- 342 total examples passing, 0 GHC warnings on the new module.
The Wai middleware wiring (extract features -> runEngine -> route by
Decision) is the next module (ML.Middleware); Engine intentionally
stays pure / independent of HTTP machinery so it can be unit-tested
without request fixtures.
- New Aenebris.ML.IForest module: pure Isolation Forest scorer
implementing the Liu et al. 2008 ICDM formula
anomaly_score = 2^(-E[h(x)] / c(n))
where E[h(x)] is the average path length across iTrees and
c(n) = 2*H(n-1) - 2(n-1)/n (expected unsuccessful BST search depth).
ITree ADT (ITreeLeaf size | ITreeSplit featIdx threshold left right)
with leaf-size c(n) correction added to traversal depth at every
leaf, matching the original paper. Score-only mode (accept pre-
trained forests from Python sklearn or similar); fitting is deferred
to a later phase. Default constants from Liu et al.: 100 trees,
256 subsample size, max depth ceil(log2(256)) = 8.
- 24 tests covering: harmonicNumber edge cases (H(0), H(1), H(2),
H(3), large-n asymptotic), normalizationConstant for n in {0, 1,
2, 256}, single-split and deep-tree path length traversal,
scoreIForest edge cases (empty forest, subsample 0, subsample 1),
shorter-path-equals-higher-score invariant, default constants,
Euler-Mascheroni precision.
- aenebris.cabal: expose Aenebris.ML.IForest in the library stanza.
- test/Spec.hs: import Expectation explicitly from Test.Hspec for the
shouldBeApprox helper.
- 325 total examples passing, 0 GHC warnings on the new module.
The escalation-gate composition with the calibrated GBDT score
(per docs/research/phase-2.5-ml-synthesis.md correction over the
0.8/0.2 folk-wisdom blend) is intentionally NOT in this module --
it belongs in the downstream ML.Engine module that wires Loader +
Inference + Calibration + IForest into a single decision pipeline.
- New Aenebris.ML.Inference module: pure tree-walk over the unified Tree
SoA produced by Aenebris.ML.Loader. Mirrors LightGBM C++ NumericalDecision
exactly: NaN remapped to 0 unless MissingType=NaN; MissingType=Zero/NaN
routes via default-left flag; '<=' predicate (not '<'); categorical
bitmap test using cat_boundaries slice; average_output divisor for RF
mode; sigmoid link for binary logistic objective. kZeroThreshold = 1e-35
matches LightGBM's IsZero exactly. Defensive against NaN/Inf/negative
on categorical paths. 35 tests covering leaf-only, numerical splits,
missing-value semantics, categorical routing, multi-tree sums, sigmoid
saturation, average_output, regression vs binary objectives, and
end-to-end against the disk fixture.
- New Aenebris.ML.Calibration module: pure calibration of binary
classifier outputs. Calibrator ADT (NoCalibrator | PlattCalibrator a b
| IsotonicCalibrator bp). Platt scaling uses Newton's method with
damped line search on Platt-1999 smoothed targets (N+ + 1)/(N+ + 2)
and 1/(N- + 2); numerically stable softplus-based log-loss. Isotonic
regression uses Pool Adjacent Violators on a stack with tie pre-
grouping, sorted (raw, calibrated) breakpoints, linear interpolation,
out-of-range clamping. Sub-2-sample input returns NoCalibrator. 28
tests covering all three calibrator constructors, fit edge cases,
Platt convergence on logistic data, isotonic correction of non-
monotone data, tie handling, all-same-label degenerate cases, and
lookup behavior including clamping and interpolation.
- aenebris.cabal: expose both new modules in the library stanza.
- 301 total examples passing, 0 GHC warnings on either new module.
- New Aenebris.ML.Loader module: pure ByteString -> Either ParseError Ensemble
parser for LightGBM v4 plain-text models. Header / tree-blocks / trailing-
section state machine; converts split-arrays + leaf-arrays into the unified
Tree SoA via unifyChild + complement; rejects non-v4, multi-class, and
is_linear=1; extracts embedded sigmoid scale and bare average_output flag;
runs validateEnsemble before returning.
- Frozen test fixtures under test/fixtures/ml/ (tiny_lgbm_v4.txt,
stump_lgbm_v4.txt) hand-crafted from the v4 spec annotated example. CI runs
against these with zero Python dependency.
- scripts/regen_lgbm_fixture.py: uv-runnable PEP 723 script that retrains a
tiny binary GBDT and writes to a separate
test/fixtures/ml/regen_sample_v4.txt so the frozen fixtures stay pristine.
- 30 new mlLoaderSpec tests covering happy path, header rejection, sigmoid
extraction, average_output, tree-level rejection, feature_names with '=',
and ParseError reporting. 238 total examples passing, 0 GHC warnings on
Loader.
- Add DEMO.md and screenshots for bug-bounty-platform, hash-cracker,
linux-cis-hardening-auditor, simple-port-scanner, simple-vulnerability-scanner,
systemd-persistence-scanner, base64-tool, caesar-cipher, dns-lookup,
metadata-scrubber-tool, network-traffic-analyzer, siem-dashboard
- Link DEMO.md from project READMEs
- Add .gitignore entries for DEMO-TRACKER and simple-port-scanner build dir
- Restructure haskell-reverse-proxy with DDoS, Fingerprint, ML, RateLimit, WAF,
Geo, and Honeypot modules; drop superseded research docs and old Makefile
- Refresh siem-dashboard dashboard.png and rename alerts.png to alert-detail.png
Add bomber CLI tool (Go) — scans dependencies across Go/Node/Python
ecosystems, generates SPDX 2.3 and CycloneDX 1.5 SBOMs, and matches
against OSV/NVD vulnerability databases with policy engine for CI/CD.
Add file-level docstrings to ai-threat-detection, firewall-rule-engine,
hash-cracker, linux-cis-hardening-auditor, binary-analysis-tool, and
credential-enumeration.
Dashboard: stat cards, severity bar, live alert feed, top IPs/paths.
Threats: filtered paginated table with detail slide-in panel.
Models: status banner, model cards with metrics, retrain button.
Dev: Vite dev server in Node 24 container with hot reload via volume
mount and named volume for node_modules isolation.
Prod: Multi-stage build (pnpm build → nginx) serving static files with
API/WebSocket reverse proxy to backend.
Parses multi-line CSIC 2010 HTTP request files into CSICRequest
dataclasses, converts to ParsedLogEntry with synthesized defaults,
and produces 35-dim feature vectors via the existing extraction pipeline.
Add ONNX inference engine with 3-model ensemble (autoencoder, random forest,
isolation forest), ONNX export utilities, MLflow experiment tracking, and
classifier training scripts. Extend pipeline with hybrid ML/rules detection
mode, ensemble score fusion and blending, and full test coverage for all
new components. Fix Python 3 SyntaxError in parsers.py (except clause).
Complete 5-file learn documentation for the advanced encrypted P2P chat
project covering Signal Protocol (X3DH + Double Ratchet), WebAuthn/Passkeys,
and the fullstack architecture.
Ref #51
- Add headers, badges, learn module pointers, just command runner tips, and AGPLv3 licenses etc. etc.
- Add missing Justfiles in some projects
- Update pre commit hook and add TODO
Replaced security considerations with an overview of the API Rate Limiter project, detailing its features, algorithms, and importance in preventing attacks.
CarterPerez-dev
dependabot[bot]