- 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
CarterPerez-dev