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feti-ai/
fetiai-v1-phiusiil-binclf-gnb-skl-202p

Name: fetiai-v1-phiusiil-binclf-gnb-skl-202p
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tabular-classification

binary-classification

phiusiil

naive-bayes

scikit-learn

feti-ai/phiusiil-if3070-stei-itb-2024-2025-1

mit

model-index

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fetiai-v1-phiusiil-binclf-gnb-skl-202p

Group 16 · IF3070 Foundations of Artificial Intelligence · STEI ITB

Gaussian Naive Bayes (scikit-learn) for phishing URL classification, served over HTTP as a single-model API.

console

$ curl -s localhost:8000/predict -H 'content-type: application/json' -d '{
    "features": {"URLLength": 31, "DomainLength": 25, "IsHTTPS": 1, "...": "all 49"}
  }'
{
  "model": "nb_sklearn",
  "label": 1,
  "verdict": "legitimate",
  "phishing_score": 0.0,
  "n_provided": 33,
  "n_imputed": 16,
  "coverage_ratio": 0.6735,
  "low_evidence": false
}

This is a coursework reimplementation, not a security product. It is trained on a static 2023–24 dataset, has no threat intelligence, no blocklist, and no knowledge of any campaign newer than its training data. Do not use it to decide whether a link is safe.

Algorithm

Gaussian Naive Bayes (scikit-learn) · Trained on PhiUSIIL · SMOTE · Feature Engineering

Built with

Python

scikit-learn

NumPy

pandas

SciPy

Docker

Links — Full application · Live demo · Dataset

What it does

It scores one pre-extracted feature row with one model and returns a verdict.

That is the whole scope, and the boundary is deliberate. This service has no page fetcher, no URL feature extractor and no SSRF guard, because duplicating a network-facing security control into four repositories is how the four copies drift apart. Turning a URL into the 49 features this API expects is the job of the full application, which fetches the page under a guard, extracts the features, and scores them against all four models at once.

So this is a tabular classifier, not a text one: the input is a 49-dimensional feature vector, and the URL string never reaches the model.

Route	Purpose
`POST /predict`	one row → verdict, score, and how much of the row was real
`POST /predict/batch`	up to 1000 rows
`GET /metadata`	the feature contract, the demoted features, and this model's metrics
`GET /healthz`	liveness
`GET /readyz`	ready once the golden-row self-test has passed

Quickstart

bash

make install         # venv + pinned dependencies
make selftest        # prove the artifact reproduces its recorded prediction
make serve           # http://127.0.0.1:8000  (docs at /docs)

No training step and no download step: the model is committed (669 KB under model/), so a fresh clone can serve immediately.

With Docker

make docker-build && docker run -p 8000:8000 fetiai-v1-phiusiil-binclf-gnb-skl-202p:local

The image bakes the model in rather than mounting it, so the image tag is a complete description of what the service will predict. The build fails if the artifact does not reproduce its own golden row.

The request contract

Send all 49 feature columns. null is allowed and is the expected value for a feature the caller could not determine — 12 of the 49 are permanently null, having failed the extraction agreement gate in the parent project.

jsonc

{
  "features": { "URLLength": 31, "DomainLength": 25, "IsHTTPS": 1, /* ...46 more */ },
  "url": "https://example.com/login",   // optional
  "domain": "example.com",              // optional
  "tld": "com",                         // optional
  "title": "Sign in"                    // optional
}

GET /metadata returns the exact 49 names in order. Three of them carry typos that are preserved on purpose — NoOfDegitsInURL, DegitRatioInURL, SpacialCharRatioInURL — because those names are what the training data means.

A missing feature is rejected by name; a null one is imputed. That distinction is the point: an extractor that dropped a column and an extractor that honestly could not determine a value are different faults with different fixes.

Why the response reports coverage

Imputation fills a missing feature from the training distribution, and that distribution is 92.5% legitimate. A mostly-empty row therefore does not produce a neutral prediction — it produces one biased toward legitimate, which is exactly the wrong direction for a phishing detector.

So every response carries n_provided, n_imputed, coverage_ratio and low_evidence. Without them a verdict drawn from six real values looks precisely as confident as one drawn from all 49.

The optional url, domain, tld and title fields are worth sending when you have them: 21 of the 49 features are derived from the URL string, and supplying it lets those be recomputed rather than imputed.

Results

Measured on a 28,081-row validation split. The held-out file shipped with the dataset has no labels, so there is no test score and none is claimed.

Model	Phishing recall	Phishing precision	Accuracy
Gaussian Naive Bayes (scikit-learn)	0.888	0.877	0.98191

Read that accuracy against 0.9248. The corpus is 92.48% legitimate, so answering "legitimate" to everything scores 0.9248 while catching no phishing whatsoever. Accuracy alone cannot tell a working detector from a constant; phishing recall can.

Class 0 is phishing and is the positive class throughout.

The scikit-learn and from-scratch implementations of this algorithm disagree on 1.1289% of the validation split. That number is the reason both exist: a reimplementation with nothing to check it against is an assertion, not a result. The counterpart lives in fetiai-v1-phiusiil-binclf-gnb-scratch-198p.

model/metrics.json also carries the legacy profile, flagged "leaky": true. It reconstructs the original notebook's configuration, which standardised each split by its own mean and standard deviation — information no deployed model can have, since there is no batch to average over when a single row arrives. It is kept as evidence of what the leak was worth and is never presented as this model's result.

What is in this repo

Path	What it is
`model/nb_sklearn.joblib`	the trained model (202 parameters)
`model/fitted_stats.json`	not optional — the scaler, imputation values, clip bounds and mode tables
`model/manifest.json`	sha256 of every file above, verified at load
`model/golden_row.json`	one record with its expected vector and prediction
`phiusiil/`	the scoring path: schema, preprocessing, and this one model class
`server/`	loader, prediction, HTTP layer

fitted_stats.json deserves the emphasis. The model alone cannot classify anything: it was fitted on standardised inputs, and the numbers that produce that standardisation live in that file. Publishing weights without it would be publishing something unusable.

It is plain JSON rather than a pickled transformer on purpose. A pickled estimator arrives with a fit method attached, and the defect this whole pipeline exists to avoid is someone calling it at serving time. Numbers that cannot be re-fitted cannot leak.

The model file is a pickle. joblib.load executes code, so treat it as you would any executable, and note that it was produced by scikit-learn 1.9.0 — loading it under a different version is unsupported. It holds a bare scikit-learn estimator rather than a wrapper class, so unpickling depends on scikit-learn alone and on nothing defined in this repository. If you want a model that loads without executing anything, the from-scratch counterpart in fetiai-v1-phiusiil-binclf-gnb-scratch-198p is plain a JSON table of per-class means and standard deviations.

Verification

bash

make selftest    # golden row, offline
make test        # golden row + HTTP contract + naming
make namecheck   # provenance hygiene

The check that carries the weight is the golden row: one real record, its expected 49-feature vector, and its expected prediction, pushed through the whole path and compared exactly. The vector and the prediction are asserted separately, because a wrong vector means the preprocessing drifted while a right vector with a wrong label means the model artifact did — different faults, different fixes.

Equality is bitwise on float32, never a tolerance. A tolerance-based comparison would pass while a fitted statistic quietly differed, which is the one thing the test exists to catch.

origin.json records where every copied file came from, including the parent bundle's own hashes, so drift is detectable without the parent repository present.

Licence and data

MIT, as in the parent project. See LICENSE.

This model was trained in part on the PhiUSIIL Phishing URL Dataset (Prasad & Chandra), available from the UCI Machine Learning Repository, licensed under CC BY 4.0.

The dataset is the UCI PhiUSIIL Phishing URL Dataset (ID 967).