Add SBAN stacking model bundle and inference entrypoint

LICENSE

@@ -0,0 +1,190 @@
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README.md

@@ -1,3 +1,194 @@
 ---
 license: apache-2.0
+library_name: sklearn
+tags:
+  - malware
+  - multi-class-classification
+  - stacking
+  - sban
+pipeline_tag: text-classification
 ---
+
+# MalwareDatasetClassification (SBAN)
+
+**[Türkçe dokümantasyon](README.tr.md)**
+
+Multiclass machine learning pipeline for **malware dataset origin classification** on the [SBAN](https://github.com/ma-soreto/SBAN) benchmark: given four synchronized representations of the same sample, predict which source corpus it belongs to among `bodmas`, `dike`, `malwarebazaar`, and `sorel20m`.
+
+This repository ships **code, notebooks, and the trained model weights**. It does **not** redistribute SBAN data or train/validation/test parquet files. You must acquire SBAN under its own terms and run the pipeline locally.
+
+| Resource | Location |
+|----------|----------|
+| Source code | GitHub: [MalwareDatasetClassification](https://github.com/berkecanalan/MalwareDatasetClassification) |
+| Model weights | Same repo root + Hugging Face: [MalwareDatasetClassification](https://huggingface.co/berkecanalan/MalwareDatasetClassification) |
+| License | Apache-2.0 ([LICENSE](LICENSE)) |
+
+---
+
+## Problem formulation
+
+- **Input:** One malware sample described by four text fields (assembly listing, binary hex dump, source code, natural-language description NLD).
+- **Output:** One of four dataset labels (which SBAN sub-corpus the sample comes from).
+- **Note:** This is **dataset / provenance classification**, not a generic “malware vs benign” detector.
+
+Reported metrics on held-out splits (from `inference.ipynb` / Colab run with exported bundle):
+
+| Split | Accuracy | Macro F1 |
+|-------|----------|----------|
+| Validation | 0.9413 | 0.9097 |
+| Test | (run locally with your SBAN test parquet) | |
+
+Per-class validation F1 (macro average 0.9097): strong on `bodmas` and `sorel20m`; smaller classes `dike` and `malwarebazaar` are harder (typical class imbalance).
+
+---
+
+## Model architecture (production bundle)
+
+The released artifact `sban_weighted_stacking_model.joblib` is produced in `svc_sban-temiz.ipynb`. High-level flow:
+
+```text
+For each representation r in {asm, binary, source, nld}:
+  raw text → TF-IDF (+ binary byte-tokenization for hex)
+           → numeric stats (char/line/token counts, entropy, …)
+           → StandardScaler on numeric block
+           → sparse hstack → feature selection (selected_indices)
+           → LinearSVC base model → decision_function scores
+
+Meta layer:
+  concat(all base decision scores + all scaled numeric blocks)
+  → HistGradientBoostingClassifier (weighted / tuned per representation)
+  → argmax → label_encoder inverse → prediction + predict_proba
+```
+
+The joblib dict (`bundle_version: 1`) includes:
+
+- `representation_order`, `representation_columns`, `numeric_feature_names`
+- `label_encoder`, `meta_model`
+- per-representation: `vectorizer`, `numeric_scaler`, `selected_indices`, `base_model`
+- metadata: `sklearn_version`, `random_state`, `selected_class_weight_configs`
+
+**Binary representation:** hex lines are parsed into space-separated byte tokens with `instsep` line separators before TF-IDF (see `inference.py`).
+
+---
+
+## Data schema (inference)
+
+Parquet or DataFrame rows must include:
+
+| Column | Role |
+|--------|------|
+| `ID` | Sample identifier (optional for inference, kept in output) |
+| `dataset_name` | Ground-truth label (only for evaluation) |
+| `assembly_code` | Assembly text |
+| `binary_code` | Hex dump text |
+| `source_code` | Source text |
+| `NLD` | Natural language description |
+
+---
+
+## Repository layout
+
+| Path | Purpose |
+|------|---------|
+| `01_make_a_dataframe.py` | Build unified SBAN parquet from raw JSON layout under `data/` |
+| `02_validate_data.py` | Validation checks |
+| `03_make_clean_dataframe.py` | Cleaning |
+| `04_analyze_prompt_residue.py` | Prompt residue analysis |
+| `05_split_dataframe.py` | Train/val/test split |
+| `06_make_features.py` | TF-IDF feature export to `features/` (sparse npz) |
+| `07_audit_source_code.py` | Source audit |
+| `08_clean_source_code.py` | Source cleaning |
+| `09_audit_assembly_code.py` | Assembly audit |
+| `10_audit_binary_code.py` | Binary audit |
+| `11_audit_nld.py` | NLD audit |
+| `baseline.ipynb` | Baseline stacking experiments |
+| `svc_sban-temiz.ipynb` | Final weighted stacking training + **model export** |
+| `inference.ipynb` | Colab/local inference, metrics, **demo row** |
+| `inference.py` | Canonical Python inference + CLI |
+| `sban_weighted_stacking_model.joblib` | Exported model (~6.4 MB) — add to clone if not using Git LFS |
+
+Generated paths (`data/`, `*.parquet`, `features/`, `reports/`) are gitignored.
+
+---
+
+## Installation
+
+**Inference only** (recommended for downstream use):
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements-inference.txt
+```
+
+**Full pipeline + notebooks:**
+
+```bash
+pip install -r requirements.txt
+```
+
+Use **scikit-learn 1.6.1** (pinned) when loading the bundle to match training.
+
+---
+
+## Inference
+
+### Command line
+
+```bash
+python inference.py \
+  --model-path sban_weighted_stacking_model.joblib \
+  --input /path/to/SBAN_val.parquet \
+  --output predictions.parquet \
+  --evaluate
+```
+
+Output columns: all input columns plus `prediction` and `prob_<class>` for each label.
+
+### Python API
+
+```python
+import pandas as pd
+from inference import load_predictor
+
+predictor = load_predictor("sban_weighted_stacking_model.joblib")
+df = pd.read_parquet("/path/to/samples.parquet")
+result = predictor.predict(df)
+```
+
+### Notebook
+
+Open `inference.ipynb`: run **Colab** or **local** setup, then the demo cell (single synthetic row). Point `VAL_PATH` / `TEST_PATH` to your SBAN splits for full evaluation.
+
+---
+
+## Reproducing training
+
+1. Place SBAN raw files under `data/M1/SBAN-MA-JUN25` (see `01_make_a_dataframe.py`).
+2. Run numbered scripts through `06_make_features.py` as needed for your experiment.
+3. Train and export with `svc_sban-temiz.ipynb` (Colab-oriented; expects parquet splits and writes `sban_weighted_stacking_model.joblib`).
+
+---
+
+## Hugging Face
+
+Upload the model repository with:
+
+- `sban_weighted_stacking_model.joblib`
+- `README.md` (this file — YAML front matter is model-card compatible)
+- `inference.py`, `requirements-inference.txt`, `LICENSE`
+
+Select **Apache-2.0** as the Hub license to match this repo.
+
+---
+
+## Citation and data
+
+- **SBAN dataset:** cite and obtain data from the official SBAN project; this repo does not host samples.
+- **This model/code:** if you use the weights or pipeline, cite this repository and respect Apache-2.0.
+
+---
+
+## Security note
+
+`joblib.load` executes pickled objects. Only load `sban_weighted_stacking_model.joblib` from this project or your own trusted export.

inference.py

@@ -0,0 +1,277 @@
+"""
+Stacking classifier inference for SBAN multi-representation malware dataset classification.
+
+Loads sban_weighted_stacking_model.joblib and predicts which source dataset
+(bodmas, dike, malwarebazaar, sorel20m) each sample belongs to.
+"""
+
+from __future__ import annotations
+
+import argparse
+import math
+import re
+from collections import Counter
+from pathlib import Path
+
+import joblib
+import numpy as np
+import pandas as pd
+from scipy.sparse import csr_matrix, hstack
+from sklearn.metrics import (
+    accuracy_score,
+    classification_report,
+    f1_score,
+)
+
+PROJECT_DIR = Path(__file__).resolve().parent
+DEFAULT_MODEL_PATH = PROJECT_DIR / "sban_weighted_stacking_model.joblib"
+
+ID_COLUMN = "ID"
+LABEL_COLUMN = "dataset_name"
+
+REPRESENTATION_COLUMNS = {
+    "asm": "assembly_code",
+    "binary": "binary_code",
+    "source": "source_code",
+    "nld": "NLD",
+}
+
+NUMERIC_FEATURE_NAMES = [
+    "char_count",
+    "line_count",
+    "token_count",
+    "avg_line_length",
+    "unique_token_ratio",
+    "char_entropy",
+]
+
+
+def binary_to_byte_tokens(text: str) -> str:
+    tokens: list[str] = []
+
+    for line in text.splitlines():
+        line = re.sub(r"\s+", "", line)
+
+        if not line or re.fullmatch(r"[0-9a-fA-F]+", line) is None:
+            continue
+
+        byte_count = len(line) // 2
+        tokens.extend(line[i * 2 : (i + 1) * 2].lower() for i in range(byte_count))
+        tokens.append("instsep")
+
+    return " ".join(tokens)
+
+
+def calculate_char_entropy(text: str) -> float:
+    if not text:
+        return 0.0
+
+    counts = Counter(text)
+    length = len(text)
+
+    return -sum(
+        (count / length) * math.log2(count / length) for count in counts.values()
+    )
+
+
+def extract_numeric_features(text: str) -> list[float]:
+    lines = text.splitlines()
+    tokens = text.split()
+
+    char_count = len(text)
+    line_count = len(lines)
+    token_count = len(tokens)
+
+    avg_line_length = (
+        sum(len(line) for line in lines) / line_count if line_count else 0.0
+    )
+
+    unique_token_ratio = (
+        len(set(tokens)) / token_count if token_count else 0.0
+    )
+
+    return [
+        float(char_count),
+        float(line_count),
+        float(token_count),
+        float(avg_line_length),
+        float(unique_token_ratio),
+        calculate_char_entropy(text),
+    ]
+
+
+def build_numeric_features(series: pd.Series) -> np.ndarray:
+    return np.asarray(
+        [extract_numeric_features(text) for text in series.astype(str)],
+        dtype=np.float32,
+    )
+
+
+def build_inference_features(
+    representation_name: str,
+    text_series: pd.Series,
+    representation_bundle: dict,
+) -> tuple[csr_matrix, np.ndarray]:
+    texts = text_series.fillna("").astype(str).tolist()
+
+    if representation_name == "binary":
+        tfidf_texts = [binary_to_byte_tokens(text) for text in texts]
+    else:
+        tfidf_texts = texts
+
+    vectorizer = representation_bundle["vectorizer"]
+    numeric_scaler = representation_bundle["numeric_scaler"]
+
+    x_tfidf = vectorizer.transform(tfidf_texts)
+    x_numeric = build_numeric_features(text_series)
+    x_numeric_scaled = numeric_scaler.transform(x_numeric)
+
+    x_full = hstack(
+        [x_tfidf, csr_matrix(x_numeric_scaled)],
+        format="csr",
+        dtype=np.float32,
+    )
+
+    return x_full, np.asarray(x_numeric_scaled, dtype=np.float32)
+
+
+class StackingPredictor:
+    """Wrapper around the exported joblib inference bundle."""
+
+    def __init__(self, model_path: Path | str = DEFAULT_MODEL_PATH) -> None:
+        self.model_path = Path(model_path)
+        self.model_bundle = joblib.load(self.model_path)
+        self.representation_order = self.model_bundle["representation_order"]
+        self.representation_columns = self.model_bundle["representation_columns"]
+        self.label_encoder = self.model_bundle["label_encoder"]
+        self.meta_model = self.model_bundle["meta_model"]
+        self.representation_bundles = self.model_bundle["representations"]
+
+    @property
+    def classes(self) -> list[str]:
+        return self.label_encoder.classes_.tolist()
+
+    def predict(self, df: pd.DataFrame) -> pd.DataFrame:
+        missing = [
+            col
+            for rep in self.representation_order
+            if (col := self.representation_columns[rep]) not in df.columns
+        ]
+        if missing:
+            raise ValueError(f"Missing required columns: {missing}")
+
+        meta_features: list[np.ndarray] = []
+
+        for representation_name in self.representation_order:
+            bundle = self.representation_bundles[representation_name]
+
+            x_full, x_numeric = build_inference_features(
+                representation_name,
+                df[self.representation_columns[representation_name]],
+                bundle,
+            )
+
+            x_selected = x_full[:, bundle["selected_indices"]]
+            decision_scores = bundle["base_model"].decision_function(x_selected)
+
+            meta_features.append(decision_scores)
+            meta_features.append(x_numeric)
+
+        x_meta = np.hstack(meta_features)
+
+        encoded_predictions = self.meta_model.predict(x_meta)
+        prediction_probabilities = self.meta_model.predict_proba(x_meta)
+
+        result = df.copy()
+        result["prediction"] = self.label_encoder.inverse_transform(
+            encoded_predictions
+        )
+
+        for index, class_name in enumerate(self.label_encoder.classes_):
+            result[f"prob_{class_name}"] = prediction_probabilities[:, index]
+
+        return result
+
+
+def load_predictor(model_path: Path | str = DEFAULT_MODEL_PATH) -> StackingPredictor:
+    return StackingPredictor(model_path)
+
+
+def evaluate_predictions(
+    predictions: pd.DataFrame,
+    label_column: str = LABEL_COLUMN,
+) -> None:
+    if label_column not in predictions.columns:
+        print(f"Skip evaluation: column {label_column!r} not in input.")
+        return
+
+    y_true = predictions[label_column]
+    y_pred = predictions["prediction"]
+
+    print(f"Accuracy    : {accuracy_score(y_true, y_pred):.6f}")
+    print(f"Macro F1    : {f1_score(y_true, y_pred, average='macro'):.6f}")
+    print(f"Weighted F1 : {f1_score(y_true, y_pred, average='weighted'):.6f}")
+    print("\nClassification Report\n")
+    print(
+        classification_report(
+            y_true,
+            y_pred,
+            digits=4,
+            target_names=sorted(y_true.unique()),
+        )
+    )
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Run SBAN weighted stacking inference on a parquet file.",
+    )
+    parser.add_argument(
+        "--model-path",
+        type=Path,
+        default=DEFAULT_MODEL_PATH,
+        help="Path to sban_weighted_stacking_model.joblib",
+    )
+    parser.add_argument(
+        "--input",
+        type=Path,
+        required=True,
+        help="Input parquet with representation columns",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        required=True,
+        help="Output parquet with prediction and prob_* columns",
+    )
+    parser.add_argument(
+        "--evaluate",
+        action="store_true",
+        help="Print metrics when dataset_name column is present",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    if not args.model_path.is_file():
+        raise FileNotFoundError(f"Model not found: {args.model_path}")
+    if not args.input.is_file():
+        raise FileNotFoundError(f"Input not found: {args.input}")
+
+    predictor = load_predictor(args.model_path)
+    df = pd.read_parquet(args.input)
+    predictions = predictor.predict(df)
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    predictions.to_parquet(args.output, index=False)
+
+    print(f"Wrote {len(predictions)} rows to {args.output}")
+
+    if args.evaluate:
+        evaluate_predictions(predictions)
+
+
+if __name__ == "__main__":
+    main()

requirements-inference.txt

@@ -0,0 +1,6 @@
+joblib==1.5.3
+numpy==2.0.2
+pandas==2.2.2
+pyarrow==18.1.0
+scikit-learn==1.6.1
+scipy==1.16.3

sban_weighted_stacking_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:85b20ae6a6ef98c5dd0891ca42f95f298c5f01240f2b998bb74283e0e8bac6ed
+size 6711061