Initial release: XGBoost + MLP for malware execution phase classification

README.md

@@ -0,0 +1,438 @@
+---
+license: cc-by-nc-4.0
+library_name: pytorch
+tags:
+  - cybersecurity
+  - malware
+  - malware-behaviour
+  - sandbox-analysis
+  - edr
+  - tabular-classification
+  - synthetic-data
+  - xgboost
+  - baseline
+pipeline_tag: tabular-classification
+base_model: []
+datasets:
+  - xpertsystems/cyb003-sample
+metrics:
+  - accuracy
+  - f1
+  - roc_auc
+model-index:
+  - name: cyb003-baseline-classifier
+    results:
+      - task:
+          type: tabular-classification
+          name: 10-class malware execution phase classification
+        dataset:
+          type: xpertsystems/cyb003-sample
+          name: CYB003 Synthetic Malware Behaviour & Classification Dataset (Sample)
+        metrics:
+          - type: roc_auc
+            value: 0.9792
+            name: Test macro ROC-AUC OvR (XGBoost, seed 42)
+          - type: accuracy
+            value: 0.9178
+            name: Test accuracy (XGBoost, seed 42)
+          - type: f1
+            value: 0.7781
+            name: Test macro-F1 (XGBoost, seed 42)
+          - type: accuracy
+            value: 0.905
+            name: Multi-seed accuracy mean ± 0.010 (XGBoost, 10 seeds)
+          - type: roc_auc
+            value: 0.975
+            name: Multi-seed ROC-AUC mean ± 0.002 (XGBoost, 10 seeds)
+          - type: roc_auc
+            value: 0.9681
+            name: Test macro ROC-AUC OvR (MLP, seed 42)
+          - type: accuracy
+            value: 0.8222
+            name: Test accuracy (MLP, seed 42)
+          - type: f1
+            value: 0.7072
+            name: Test macro-F1 (MLP, seed 42)
+---
+
+# CYB003 Baseline Classifier
+
+**Malware execution-phase classifier trained on the CYB003 synthetic
+malware behaviour sample. Predicts which of 10 execution phases a
+per-timestep telemetry record belongs to, from observable behavioural
+and PE-static features.**
+
+> **Baseline reference, not for production use.** This model demonstrates
+> that the [CYB003 sample dataset](https://huggingface.co/datasets/xpertsystems/cyb003-sample)
+> is learnable end-to-end and gives prospective buyers a working starting
+> point. It is not a production sandbox, EDR, or threat-detection system.
+> See [Limitations](#limitations).
+
+## Model overview
+
+| Property | Value |
+|---|---|
+| Task | 10-class execution_phase classification |
+| Training data | `xpertsystems/cyb003-sample` (6,000 timesteps across 100 malware samples) |
+| Models | XGBoost + PyTorch MLP |
+| Input features | 69 (after one-hot encoding) |
+| Split | **Group-aware by sample_id** (disjoint train/val/test samples) |
+| Validation | Single seed (artifact) + multi-seed aggregate across 10 seeds |
+| License | CC-BY-NC-4.0 (matches dataset) |
+| Status | Reference baseline |
+
+## Why this task instead of malware family classification?
+
+The CYB003 dataset README leads with "training malware family classifiers"
+as a suggested use case. We piloted that target first and found it is
+**not learnable from the sample dataset** under proper group-aware
+evaluation: with only 100 unique samples spread across 10 families,
+XGBoost on per-timestep features lands at ~15% accuracy and ROC-AUC ~0.58
+— at majority baseline. Per-sample aggregation gives the same result.
+
+This is a **sample-size constraint**, not a feature-engineering failure.
+With ~7 samples per family on average, a held-out test set of 15 samples
+covers at most ~8 families and yields a model that cannot generalize.
+The full 280k-row CYB003 product, with ~28 samples per family at the
+sample's distribution, will not have this constraint.
+
+We pivoted to **execution_phase prediction**, which has 6,000 rows of
+per-timestep data and learns cleanly: 91% accuracy, ROC-AUC 0.98, stable
+across seeds. This is a legitimate SOC use case — dynamic-analysis tools
+and EDR systems regularly need to tag what phase of execution observed
+malware activity belongs to — and it shows the dataset is well-calibrated
+even when the headline product use case needs more data.
+
+Two model artifacts are published. They are designed to be used together — disagreement is a useful triage signal:
+
+- `model_xgb.json` — gradient-boosted trees, primary recommendation
+- `model_mlp.safetensors` — PyTorch MLP in SafeTensors format
+
+## Quick start
+
+```bash
+pip install xgboost torch safetensors pandas huggingface_hub
+```
+
+```python
+from huggingface_hub import hf_hub_download
+import json, numpy as np, torch, xgboost as xgb
+from safetensors.torch import load_file
+
+REPO = "xpertsystems/cyb003-baseline-classifier"
+
+paths = {n: hf_hub_download(REPO, n) for n in [
+    "model_xgb.json", "model_mlp.safetensors",
+    "feature_engineering.py", "feature_meta.json", "feature_scaler.json",
+]}
+
+import sys, os
+sys.path.insert(0, os.path.dirname(paths["feature_engineering.py"]))
+from feature_engineering import transform_single, load_meta, INT_TO_LABEL
+
+meta = load_meta(paths["feature_meta.json"])
+xgb_model = xgb.XGBClassifier(); xgb_model.load_model(paths["model_xgb.json"])
+
+# Predict (see inference_example.ipynb for the full pattern)
+X = transform_single(my_timestep_record, meta)
+proba = xgb_model.predict_proba(X)[0]
+print(INT_TO_LABEL[int(np.argmax(proba))])
+```
+
+See [`inference_example.ipynb`](./inference_example.ipynb) for the full
+copy-paste demo.
+
+## Training data
+
+Trained on the public sample of CYB003, 6,000 per-timestep telemetry
+rows from 100 malware samples (60 timesteps per sample):
+
+| Phase | Total rows | Train share | Test rows (seed 42) |
+|---|---:|---:|---:|
+| `initial_drop` | 801 | 13.4% | 120 |
+| `lateral_movement` | 799 | 13.3% | 120 |
+| `persistence_establishment` | 787 | 13.1% | 119 |
+| `data_exfiltration` | 783 | 13.1% | 100 |
+| `c2_communication` | 709 | 11.8% | 87 |
+| `privilege_escalation` | 705 | 11.8% | 107 |
+| `payload_execution` | 705 | 11.8% | 109 |
+| `dormancy_dwell` | 250 | 4.2% | 83 |
+| `sandbox_evasion_stall` | 234 | 3.9% | 32 |
+| `self_destruct_cleanup` | 227 | 3.8% | 23 |
+
+### Group-aware split
+
+A single malware sample generates 60 highly-correlated timesteps. Random
+row-level splitting would put timesteps from the same sample in both
+train and test, inflating metrics in a way that does not generalize to
+new samples.
+
+This release uses **GroupShuffleSplit by `sample_id`** (nested, 70/15/15):
+
+| Fold | Samples | Timesteps |
+|---|---:|---:|
+| Train | 69 | 4,140 |
+| Validation | 16 | 960 |
+| Test | 15 | 900 |
+
+All test samples are completely unseen during training. Class imbalance
+is addressed with `class_weight='balanced'` (XGBoost `sample_weight`) and
+weighted cross-entropy (MLP).
+
+## Feature pipeline
+
+The bundled `feature_engineering.py` is the canonical feature recipe.
+69 features survive after encoding, drawn from:
+
+- **Per-timestep numeric** (10): `timestep`, `api_call_rate`, `registry_write_count`, `network_connection_count`, `process_injection_flag`, `c2_beacon_interval_sec`, `av_signature_hit_flag`, `sandbox_evasion_flag`, `lateral_propagation_count`, `privilege_escalation_flag`
+- **PE static features** (11): `pe_entropy_mean`, `pe_entropy_std`, `import_hash_cluster`, `section_count`, `packed_section_ratio`, `string_entropy_mean`, `byte_histogram_chi2`, `code_section_rx_ratio`, `resource_section_entropy`, `suspicious_import_count`, `packer_detected_flag`
+- **Categorical** (6, one-hot encoded): `malware_family`, `threat_actor_tier`, `target_platform`, `obfuscation_technique`, `detection_outcome`, `ep_stack`
+- **Engineered** (6): `api_burst_score`, `is_c2_active`, `is_high_net_volume`, `is_stealth_step`, `is_destructive_step`, `lateral_activity_score`
+
+### Leakage audit
+
+No categorical feature has phase->phase purity above 0.17 (uniform
+random baseline is 0.10), so nothing in the dataset is an oracle for
+the target. The model relies on a mix of `timestep` (strong but not
+deterministic) and behavioural features.
+
+## Evaluation
+
+### Test-set metrics, seed 42 (n = 900 timesteps from 15 disjoint samples)
+
+**XGBoost** (the published `model_xgb.json` artifact)
+
+| Metric | Value |
+|---|---:|
+| Macro ROC-AUC (OvR) | **0.9792** |
+| Accuracy | **0.9178** |
+| Macro-F1 | 0.7781 |
+| Weighted-F1 | 0.9173 |
+
+**MLP** (the published `model_mlp.safetensors` artifact)
+
+| Metric | Value |
+|---|---:|
+| Macro ROC-AUC (OvR) | 0.9681 |
+| Accuracy | 0.8222 |
+| Macro-F1 | 0.7072 |
+| Weighted-F1 | 0.8278 |
+
+### Multi-seed robustness (XGBoost, 10 seeds)
+
+Accuracy and ROC-AUC are tight across seeds — the task is genuinely
+learnable, not seed-lucky:
+
+| Metric | Mean | Std | Min | Max |
+|---|---:|---:|---:|---:|
+| Accuracy | 0.905 | 0.010 | 0.882 | 0.921 |
+| Macro-F1 | 0.784 | 0.013 | 0.759 | 0.807 |
+| Macro ROC-AUC OvR | 0.975 | 0.002 | 0.972 | 0.979 |
+
+Full per-seed results in [`multi_seed_results.json`](./multi_seed_results.json).
+All 10 seeds yielded all 10 classes in the test fold, supporting clean
+multi-class ROC-AUC computation.
+
+### Per-class F1 (seed 42) — where the signal is and isn't
+
+| Phase | XGBoost F1 | MLP F1 | Note |
+|---|---:|---:|---|
+| `c2_communication` | **1.000** | 1.000 | Trivial: tight timestep window 52-59 + c2_beacon signal |
+| `persistence_establishment` | **0.992** | 0.870 | Tight timestep window 9-17 + registry writes |
+| `lateral_movement` | **0.992** | 0.907 | Tight timestep window 26-34 + lateral_propagation |
+| `privilege_escalation` | **0.991** | 0.915 | Tight timestep window 18-25 + privilege flag |
+| `data_exfiltration` | **0.970** | 0.918 | Tight timestep window 43-51 + network volume |
+| `payload_execution` | **0.963** | 0.698 | Tight timestep window 35-42 + API bursts |
+| `initial_drop` | **0.945** | 0.886 | Tight timestep window 0-8 |
+| `dormancy_dwell` | 0.530 | 0.520 | Hard: spans full 0-59 timestep range |
+| `self_destruct_cleanup` | 0.273 | 0.282 | Hard: spans full 0-59, low row count (227) |
+| `sandbox_evasion_stall` | 0.125 | 0.077 | Hard: spans full 0-59, low row count (234) |
+
+Seven phases are near-trivially classified because they sit in tight
+timestep windows with characteristic behavioural signatures. **Three
+phases — `dormancy_dwell`, `sandbox_evasion_stall`, `self_destruct_cleanup`
+— scatter across the full 0–59 timestep range** and lack distinctive
+behavioural features (idle/evasion phases have low activity by design),
+so a flat-tabular event-level model can't reliably disambiguate them.
+Sequence models that consider neighbouring timesteps would help here.
+
+### Ablation: which feature groups matter
+
+| Configuration | Accuracy | Macro-F1 | ROC-AUC | Δ accuracy |
+|---|---:|---:|---:|---:|
+| Full feature set (published) | 0.9178 | 0.7781 | 0.9792 | — |
+| No `timestep` | 0.6933 | 0.5963 | 0.9264 | **−0.2244** |
+| No behavioural features | 0.9089 | 0.7579 | 0.9705 | −0.0089 |
+| No PE static features | 0.9167 | 0.7808 | 0.9786 | −0.0011 |
+| No engineered features | 0.9200 | 0.7931 | 0.9797 | +0.0022 |
+
+Three clear findings:
+
+1. **`timestep` is by far the dominant feature** (drops 22 pp when removed,
+   ROC-AUC still 0.93). Malware execution progresses in time, and where
+   you are in that timeline carries most of the phase signal.
+2. **PE static features are barely used for phase prediction.** This is
+   honest: PE features (entropy, packed sections, import hashes) inform
+   family classification, not phase classification. A buyer doing family
+   work should expect to use them; for phase work they can be dropped.
+3. **Engineered features and behavioural features each contribute ~1 pp.**
+   Trees recover most of the engineered features on their own.
+
+### Architecture
+
+**XGBoost:** multi-class gradient boosting (`multi:softprob`, 10 classes),
+`hist` tree method, class-balanced sample weights, early stopping on
+validation mlogloss.
+
+**MLP:** `69 → 128 → 64 → 10`, each hidden layer followed by `BatchNorm1d`
+→ `ReLU` → `Dropout(0.3)`, weighted cross-entropy loss, AdamW optimizer,
+early stopping on validation macro-F1.
+
+Training hyperparameters (learning rate, batch size, n_estimators,
+early-stopping patience, weight decay, class-weighting strategy) are
+held internally by XpertSystems and are not part of this release.
+
+## Limitations
+
+**This is a baseline reference, not a production sandbox or threat detector.**
+
+1. **Three phases are genuinely hard at sample size.** `dormancy_dwell`,
+   `sandbox_evasion_stall`, and `self_destruct_cleanup` span the full
+   0–59 timestep range and have low row counts. Per-class F1 = 0.13–0.53.
+   These are the phases by design lacking distinctive moment-to-moment
+   features (the malware is being quiet to evade detection). Sequence
+   models or per-sample aggregation would substantially improve these.
+
+2. **The pivot away from malware family classification is dataset-limited,
+   not method-limited.** Family classification on 100 samples with 10
+   classes is at majority baseline. The full 280k-row CYB003 product
+   provides ~5,600 samples and supports proper family classification.
+
+3. **Synthetic-vs-real transfer.** The dataset is synthetic and calibrated
+   to threat-intelligence and AV-testing benchmark targets (VirusTotal,
+   AV-TEST, MITRE ATT&CK Evaluations, Mandiant M-Trends, CrowdStrike GTR,
+   Verizon DBIR). Real malware telemetry has different noise
+   characteristics, adversary adaptation, and instrumentation gaps. Do
+   not assume metrics transfer.
+
+4. **Adversarial robustness not evaluated.** The dataset is not
+   adversarially generated; the model has not been red-teamed against
+   evasive samples.
+
+5. **MLP brittleness on OOD inputs.** With ~4k training timesteps, the
+   MLP can produce confidently-wrong predictions on hand-crafted records
+   far from the training manifold. XGBoost is more robust. Use both;
+   treat disagreement as a signal for human review.
+
+6. **`timestep` dominance is a property of the dataset.** Real malware
+   in production doesn't have a clean "timestep" feature on a per-sample
+   60-step normalized timeline — that's a simulator artifact. A buyer
+   transferring this baseline to real sandbox traces would need to
+   recover an equivalent temporal-position feature from execution-trace
+   timestamps relative to detonation.
+
+## Notes on dataset schema
+
+The CYB003 sample dataset README describes some fields differently from
+the actual schema. The model was trained on the actual schema; this note
+helps buyers reconcile what they read with what they receive.
+
+| What the README says | What the data actually contains |
+|---|---|
+| `pe_entropy` (one column) | `pe_entropy_mean` + `pe_entropy_std` (two columns) |
+| `process_injection_count` | `process_injection_flag` (binary, not a count) |
+| `c2_beacon_active` | `c2_beacon_interval_sec` (seconds, 0 when inactive) |
+| `av_detected`, `edr_detected`, `sandbox_evaded`, `dwell_time_hours`, `persistence_mechanism`, `lotl_technique_used` (per-timestep) | None of these exist on per-timestep; equivalents (`av_signature_hit_flag`, `sandbox_evasion_flag`) do exist with different names |
+| `ep_stack`: 3 values (`legacy_av`, `ngav_ml_based`, `edr_full`) | `ep_stack`: 8 values (`legacy_av_only`, `ngav_ml_based`, `edr_endpoint_detect`, `av_plus_firewall`, `xdr_extended_detect`, `managed_detection_response`, `deception_honeypot`, `no_protection`) |
+| 9 malware families listed | 10 families in the data (`apt_implant` is the additional one) |
+| `coordinated_campaign_flag` (described as a flag) | Constant = 1 for all rows in the sample (uninformative) |
+
+The actual per-timestep table also contains rich PE-static features not
+listed in the README: `import_hash_cluster`, `section_count`,
+`packed_section_ratio`, `string_entropy_mean`, `byte_histogram_chi2`,
+`code_section_rx_ratio`, `resource_section_entropy`,
+`suspicious_import_count`. These are excellent features for family
+classification work and are documented in the model's
+`feature_engineering.py`.
+
+None of these discrepancies affects model correctness — the feature
+pipeline uses the actual column names. If you build your own pipeline
+against the dataset, use the actual columns, not the README descriptions.
+
+## Intended use
+
+- **Evaluating fit** of the CYB003 dataset for your malware-analysis
+  or sandbox-detection research
+- **Baseline reference** for new model architectures (especially sequence
+  models, which should beat this baseline on the late/scattered phases)
+- **Teaching and demo** for tabular classification on malware telemetry
+- **Feature engineering reference** for per-timestep behavioural data
+
+## Out-of-scope use
+
+- Production sandbox analysis on real malware
+- EDR phase tagging on real systems
+- Family attribution (this baseline does not address that task; see why above)
+- Adversarial-evasion evaluation (dataset not adversarially generated)
+- Any operational security decision
+
+## Reproducibility
+
+Outputs above were produced with `seed = 42` (published artifact),
+group-aware nested `GroupShuffleSplit` (70/15/15 by sample_id), on the
+published sample (`xpertsystems/cyb003-sample`, version 1.0.0, generated
+2026-05-16). The feature pipeline in `feature_engineering.py` is
+deterministic and the trained weights in this repo correspond exactly
+to the metrics above.
+
+Multi-seed results (seeds 42, 7, 13, 17, 23, 31, 45, 99, 123, 200) in
+`multi_seed_results.json` confirm robust performance across splits.
+
+The training script itself is private to XpertSystems. The published
+artifacts contain the feature pipeline, model weights, scaler, metadata,
+and validation results — sufficient to reproduce inference but not
+training.
+
+## Files in this repo
+
+| File | Purpose |
+|---|---|
+| `model_xgb.json` | XGBoost weights (seed 42) |
+| `model_mlp.safetensors` | PyTorch MLP weights (seed 42) |
+| `feature_engineering.py` | Feature pipeline (load → engineer → encode) |
+| `feature_meta.json` | Feature column order + categorical levels |
+| `feature_scaler.json` | MLP input mean/std (XGBoost ignores) |
+| `validation_results.json` | Per-class metrics, confusion matrix, architecture |
+| `ablation_results.json` | Per-feature-group ablation (timestep, behavioural, PE static, engineered) |
+| `multi_seed_results.json` | XGBoost metrics across 10 seeds with aggregate statistics |
+| `inference_example.ipynb` | End-to-end inference demo notebook |
+| `README.md` | This file |
+
+## Contact and full product
+
+The full **CYB003** dataset contains ~349,000 rows across four files,
+with calibrated benchmark validation against 12 metrics drawn from
+authoritative threat intelligence and AV-testing sources (VirusTotal,
+AV-TEST, MITRE ATT&CK Evaluations, Mandiant, CrowdStrike, Verizon).
+The full XpertSystems.ai synthetic data catalogue spans 41 SKUs across
+Cybersecurity, Healthcare, Insurance & Risk, Oil & Gas, and Materials
+& Energy.
+
+- 📧 **pradeep@xpertsystems.ai**
+- 🌐 **https://xpertsystems.ai**
+- 🗂  Dataset: https://huggingface.co/datasets/xpertsystems/cyb003-sample
+- 🤖 Companion models:
+  - https://huggingface.co/xpertsystems/cyb001-baseline-classifier (network traffic)
+  - https://huggingface.co/xpertsystems/cyb002-baseline-classifier (ATT&CK kill-chain)
+
+## Citation
+
+```bibtex
+@misc{xpertsystems_cyb003_baseline_2026,
+  title  = {CYB003 Baseline Classifier: XGBoost and MLP for Malware Execution Phase Classification},
+  author = {XpertSystems.ai},
+  year   = {2026},
+  url    = {https://huggingface.co/xpertsystems/cyb003-baseline-classifier},
+  note   = {Baseline reference model trained on xpertsystems/cyb003-sample}
+}
+```

ablation_results.json

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+{
+  "purpose": "Quantify how much each feature group contributes to the headline XGBoost score. Identical architecture, same group-aware split, with one feature group dropped at a time.",
+  "full_model_metrics": {
+    "model": "xgboost",
+    "accuracy": 0.9177777777777778,
+    "macro_f1": 0.7780699645112974,
+    "weighted_f1": 0.9064879129227142,
+    "per_class_f1": {
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+      "data_exfiltration": 0.9699570815450643,
+      "dormancy_dwell": 0.5301204819277109,
+      "initial_drop": 0.9453125,
+      "lateral_movement": 0.9917355371900827,
+      "payload_execution": 0.963302752293578,
+      "persistence_establishment": 0.9918032786885246,
+      "privilege_escalation": 0.9907407407407407,
+      "sandbox_evasion_stall": 0.125,
+      "self_destruct_cleanup": 0.2727272727272727
+    },
+    "confusion_matrix": {
+      "labels": [
+        "c2_communication",
+        "data_exfiltration",
+        "dormancy_dwell",
+        "initial_drop",
+        "lateral_movement",
+        "payload_execution",
+        "persistence_establishment",
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+  "ablations": {
+    "no_pe_static": {
+      "n_features": 58,
+      "dropped_count": 11,
+      "metrics": {
+        "model": "xgboost_no_pe_static",
+        "accuracy": 0.9166666666666666,
+        "macro_f1": 0.7808429949060417,
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+          "initial_drop": 0.9494163424124513,
+          "lateral_movement": 0.995850622406639,
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+          "self_destruct_cleanup": 0.09302325581395349
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+            "data_exfiltration",
+            "dormancy_dwell",
+            "initial_drop",
+            "lateral_movement",
+            "payload_execution",
+            "persistence_establishment",
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+          "self_destruct_cleanup": 0.3404255319148936
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+          "labels": [
+            "c2_communication",
+            "data_exfiltration",
+            "dormancy_dwell",
+            "initial_drop",
+            "lateral_movement",
+            "payload_execution",
+            "persistence_establishment",
+            "privilege_escalation",
+            "sandbox_evasion_stall",
+            "self_destruct_cleanup"
+          ],
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+    }
+  }
+}

feature_engineering.py

@@ -0,0 +1,325 @@
+"""
+feature_engineering.py
+======================
+
+Feature pipeline for the CYB003 baseline classifier.
+
+Predicts `execution_phase` (10-class) from per-timestep malware execution
+telemetry on the CYB003 sample dataset.
+
+CSV inputs:
+    malware_samples.csv     (primary, one row per timestep, 60 timesteps
+                             per sample, 100 samples = 6000 rows)
+    sample_summary.csv      (per-sample aggregates; reserved for future
+                             work — joining inflates per-sample features
+                             across 60 identical replications, which hurt
+                             the model in pilot experiments)
+    environment_profiles.csv (reserved for future work)
+    execution_events.csv    (reserved for future work)
+
+Target classes (10 execution phases observed in the sample):
+    initial_drop, persistence_establishment, privilege_escalation,
+    lateral_movement, payload_execution, data_exfiltration,
+    c2_communication, dormancy_dwell, sandbox_evasion_stall,
+    self_destruct_cleanup
+
+This corresponds to the SOC / sandbox-analyst use case: given the malware's
+current behavioural state, what phase of execution is it in? Useful for
+dynamic-analysis tools, EDR phase tagging, and behavioural classifiers.
+
+The pivot to execution_phase (away from malware_family) happened because
+malware family classification on n=100 samples with group-aware splitting
+landed at majority-baseline accuracy (~15%, ROC-AUC ~0.58). execution_phase
+sits on 6,000 rows of per-timestep data with strong, stable signal across
+seeds (~91% accuracy, ROC-AUC ~0.98). See the model card for details.
+
+Leakage analysis
+----------------
+No categorical feature has phase->phase purity above 0.17 (uniform random
+baseline is 0.10), so nothing in the data is an oracle for the target.
+The model relies on a mix of `timestep` (strong but not deterministic —
+most phases have tight timestep windows, but `dormancy_dwell`,
+`sandbox_evasion_stall`, and `self_destruct_cleanup` span the full
+0-59 range) and behavioural features.
+
+Public API
+----------
+    build_features(samples_path) -> (X, y, groups, meta)
+    transform_single(record, meta) -> np.ndarray
+    save_meta(meta, path) / load_meta(path)
+
+License
+-------
+Ships with the public model on Hugging Face under CC-BY-NC-4.0, matching
+the dataset license. See README.md.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+# ---------------------------------------------------------------------------
+# Label space
+# ---------------------------------------------------------------------------
+
+# Alphabetical for stable indexing.
+LABEL_ORDER = [
+    "c2_communication",
+    "data_exfiltration",
+    "dormancy_dwell",
+    "initial_drop",
+    "lateral_movement",
+    "payload_execution",
+    "persistence_establishment",
+    "privilege_escalation",
+    "sandbox_evasion_stall",
+    "self_destruct_cleanup",
+]
+LABEL_TO_INT = {lbl: i for i, lbl in enumerate(LABEL_ORDER)}
+INT_TO_LABEL = {i: lbl for lbl, i in LABEL_TO_INT.items()}
+
+# ---------------------------------------------------------------------------
+# Identifier and target columns - not features
+# ---------------------------------------------------------------------------
+
+ID_COLUMNS = ["sample_id", "family_id", "threat_actor_id"]
+TARGET_COLUMN = "execution_phase"
+
+# Note: malware_family is kept as a FEATURE for phase prediction (family
+# is a useful observable - a SOC analyst knows what family they're looking
+# at). It's not a leakage source for phase since phase->family purity is
+# only 0.16. Same logic for threat_actor_tier, ep_stack, target_platform -
+# these are environmental context, not oracles for phase.
+
+# ---------------------------------------------------------------------------
+# Per-timestep numeric features
+# ---------------------------------------------------------------------------
+
+DIRECT_NUMERIC_TIMESTEP_FEATURES = [
+    "timestep",                      # strong but non-deterministic phase signal
+    "api_call_rate",
+    "registry_write_count",
+    "network_connection_count",
+    "process_injection_flag",
+    "c2_beacon_interval_sec",
+    "av_signature_hit_flag",
+    "sandbox_evasion_flag",
+    "lateral_propagation_count",
+    "privilege_escalation_flag",
+    # PE static features (constant per sample but informative for phase
+    # given that the model sees these alongside per-step behaviour)
+    "pe_entropy_mean",
+    "pe_entropy_std",
+    "import_hash_cluster",
+    "section_count",
+    "packed_section_ratio",
+    "string_entropy_mean",
+    "byte_histogram_chi2",
+    "code_section_rx_ratio",
+    "resource_section_entropy",
+    "suspicious_import_count",
+    "packer_detected_flag",
+]
+
+CATEGORICAL_TIMESTEP_FEATURES = [
+    "malware_family",          # kept as feature: phase prediction conditions
+                               # on family (a known observable in SOC workflows)
+    "threat_actor_tier",
+    "target_platform",
+    "obfuscation_technique",
+    "detection_outcome",
+    "ep_stack",
+]
+
+# ---------------------------------------------------------------------------
+# Engineered features (none derived from phase or timestep alone)
+# ---------------------------------------------------------------------------
+
+def _add_engineered_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Six engineered features. None directly encode phase (that would be
+    a tautology); each is a behavioural composite that disambiguates
+    phases sharing similar timestep ranges.
+    """
+    df = df.copy()
+
+    # 1. API burst score: high for execution-heavy phases (payload_execution,
+    #    privilege_escalation), low for stealth phases (dormancy, evasion).
+    df["api_burst_score"] = (
+        df["api_call_rate"] * df["registry_write_count"].clip(upper=50)
+    ).astype(float)
+
+    # 2. C2 active flag: positive c2_beacon_interval_sec indicates active
+    #    beaconing. Strongly correlates with c2_communication phase.
+    df["is_c2_active"] = (df["c2_beacon_interval_sec"] > 0).astype(int)
+
+    # 3. High network volume step: above-threshold connection count, common
+    #    in lateral_movement, data_exfiltration, c2_communication.
+    df["is_high_net_volume"] = (df["network_connection_count"] > 5).astype(int)
+
+    # 4. Stealth indicator: low api_call_rate AND no AV/sandbox hit. Used
+    #    to disambiguate dormancy_dwell / sandbox_evasion_stall from active
+    #    phases that happen to land in similar timestep windows.
+    df["is_stealth_step"] = (
+        (df["api_call_rate"] < 5)
+        & (df["av_signature_hit_flag"] == 0)
+        & (df["sandbox_evasion_flag"] == 0)
+    ).astype(int)
+
+    # 5. Destructive action indicator: combines privilege escalation flag
+    #    and registry-write count. High in persistence_establishment and
+    #    self_destruct_cleanup.
+    df["is_destructive_step"] = (
+        (df["privilege_escalation_flag"] == 1)
+        | (df["registry_write_count"] > 10)
+    ).astype(int)
+
+    # 6. Lateral activity: network connections combined with lateral_propagation
+    #    count > 0. Distinguishes lateral_movement from other network phases.
+    df["lateral_activity_score"] = (
+        df["lateral_propagation_count"] * df["network_connection_count"]
+    ).astype(float)
+
+    return df
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+def build_features(
+    samples_path: str | Path,
+) -> tuple[pd.DataFrame, pd.Series, pd.Series, dict[str, Any]]:
+    """
+    Load CSV, drop identifier columns and target, engineer features,
+    one-hot encode, return (X, y, groups, meta).
+
+    `groups` is a Series of sample_id values aligned with X. Use it
+    with GroupShuffleSplit / GroupKFold: a single sample contains 60
+    correlated timesteps, and row-level random splitting inflates metrics.
+    """
+    samples = pd.read_csv(samples_path)
+
+    # Extract target + groups
+    y = samples[TARGET_COLUMN].map(LABEL_TO_INT)
+    if y.isna().any():
+        bad = samples.loc[y.isna(), TARGET_COLUMN].unique()
+        raise ValueError(f"Unknown execution_phase values: {bad}")
+    y = y.astype(int)
+    groups = samples["sample_id"].copy()
+
+    # Drop target + identifiers from feature pool
+    samples = samples.drop(columns=ID_COLUMNS + [TARGET_COLUMN], errors="ignore")
+
+    # Engineered features
+    samples = _add_engineered_features(samples)
+
+    # Numeric features
+    numeric_features = (
+        DIRECT_NUMERIC_TIMESTEP_FEATURES
+        + [
+            "api_burst_score", "is_c2_active", "is_high_net_volume",
+            "is_stealth_step", "is_destructive_step", "lateral_activity_score",
+        ]
+    )
+    X_numeric = samples[numeric_features].astype(float)
+
+    # One-hot categoricals
+    categorical_levels: dict[str, list[str]] = {}
+    blocks: list[pd.DataFrame] = []
+    for col in CATEGORICAL_TIMESTEP_FEATURES:
+        if col not in samples.columns:
+            continue
+        levels = sorted(samples[col].dropna().unique().tolist())
+        categorical_levels[col] = levels
+        block = pd.get_dummies(
+            samples[col].astype("category").cat.set_categories(levels),
+            prefix=col, dummy_na=False,
+        ).astype(int)
+        blocks.append(block)
+
+    X = pd.concat(
+        [X_numeric.reset_index(drop=True)]
+        + [b.reset_index(drop=True) for b in blocks],
+        axis=1,
+    ).fillna(0.0)
+
+    meta = {
+        "feature_names": X.columns.tolist(),
+        "numeric_features": numeric_features,
+        "categorical_levels": categorical_levels,
+        "label_to_int": LABEL_TO_INT,
+        "int_to_label": INT_TO_LABEL,
+    }
+    return X, y, groups, meta
+
+
+def transform_single(
+    record: dict | pd.DataFrame,
+    meta: dict[str, Any],
+) -> np.ndarray:
+    """Encode a single timestep record for inference."""
+    if isinstance(record, dict):
+        df = pd.DataFrame([record.copy()])
+    else:
+        df = record.copy()
+
+    df = _add_engineered_features(df)
+
+    numeric = pd.DataFrame({
+        col: df.get(col, pd.Series([0.0] * len(df))).astype(float).values
+        for col in meta["numeric_features"]
+    })
+    blocks: list[pd.DataFrame] = [numeric]
+    for col, levels in meta["categorical_levels"].items():
+        val = df.get(col, pd.Series([None] * len(df)))
+        block = pd.get_dummies(
+            val.astype("category").cat.set_categories(levels),
+            prefix=col, dummy_na=False,
+        ).astype(int)
+        for lvl in levels:
+            cname = f"{col}_{lvl}"
+            if cname not in block.columns:
+                block[cname] = 0
+        block = block[[f"{col}_{lvl}" for lvl in levels]]
+        blocks.append(block)
+
+    X = pd.concat(blocks, axis=1).fillna(0.0)
+    X = X.reindex(columns=meta["feature_names"], fill_value=0.0)
+    return X.values.astype(np.float32)
+
+
+def save_meta(meta: dict[str, Any], path: str | Path) -> None:
+    serializable = {
+        "feature_names": meta["feature_names"],
+        "numeric_features": meta["numeric_features"],
+        "categorical_levels": meta["categorical_levels"],
+        "label_to_int": meta["label_to_int"],
+        "int_to_label": {str(k): v for k, v in meta["int_to_label"].items()},
+    }
+    with open(path, "w") as f:
+        json.dump(serializable, f, indent=2)
+
+
+def load_meta(path: str | Path) -> dict[str, Any]:
+    with open(path) as f:
+        meta = json.load(f)
+    meta["int_to_label"] = {int(k): v for k, v in meta["int_to_label"].items()}
+    return meta
+
+
+if __name__ == "__main__":
+    import sys
+    base = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("/mnt/user-data/uploads")
+    X, y, groups, meta = build_features(base / "malware_samples.csv")
+    print(f"X shape: {X.shape}")
+    print(f"y shape: {y.shape}")
+    print(f"groups: {groups.nunique()} samples")
+    print(f"n features: {len(meta['feature_names'])}")
+    print(f"label distribution:\n{y.map(INT_TO_LABEL).value_counts()}")
+    print(f"X has NaN: {X.isnull().any().any()}")

feature_meta.json

@@ -0,0 +1,182 @@
+{
+  "feature_names": [
+    "timestep",
+    "api_call_rate",
+    "registry_write_count",
+    "network_connection_count",
+    "process_injection_flag",
+    "c2_beacon_interval_sec",
+    "av_signature_hit_flag",
+    "sandbox_evasion_flag",
+    "lateral_propagation_count",
+    "privilege_escalation_flag",
+    "pe_entropy_mean",
+    "pe_entropy_std",
+    "import_hash_cluster",
+    "section_count",
+    "packed_section_ratio",
+    "string_entropy_mean",
+    "byte_histogram_chi2",
+    "code_section_rx_ratio",
+    "resource_section_entropy",
+    "suspicious_import_count",
+    "packer_detected_flag",
+    "api_burst_score",
+    "is_c2_active",
+    "is_high_net_volume",
+    "is_stealth_step",
+    "is_destructive_step",
+    "lateral_activity_score",
+    "malware_family_apt_implant",
+    "malware_family_botnet_agent",
+    "malware_family_cryptominer",
+    "malware_family_dropper",
+    "malware_family_fileless_malware",
+    "malware_family_ransomware",
+    "malware_family_rootkit",
+    "malware_family_spyware",
+    "malware_family_trojan",
+    "malware_family_worm",
+    "threat_actor_tier_apt",
+    "threat_actor_tier_commodity",
+    "threat_actor_tier_crimeware",
+    "threat_actor_tier_nation_state",
+    "target_platform_android_13",
+    "target_platform_embedded_ot_firmware",
+    "target_platform_linux_rhel_9",
+    "target_platform_linux_ubuntu_22",
+    "target_platform_macos_ventura",
+    "target_platform_windows_10_enterprise",
+    "target_platform_windows_11_pro",
+    "target_platform_windows_server_2022",
+    "obfuscation_technique_anti_analysis_stall",
+    "obfuscation_technique_code_signing_abuse",
+    "obfuscation_technique_lotl_binary",
+    "obfuscation_technique_packing",
+    "obfuscation_technique_polymorphic_mutation",
+    "obfuscation_technique_sandbox_evasion",
+    "obfuscation_technique_string_encryption",
+    "detection_outcome_behavioural_flag",
+    "detection_outcome_definitive_detection",
+    "detection_outcome_heuristic_alert",
+    "detection_outcome_sandbox_evasion_confirmed",
+    "detection_outcome_signature_miss",
+    "ep_stack_av_plus_firewall",
+    "ep_stack_deception_honeypot",
+    "ep_stack_edr_endpoint_detect",
+    "ep_stack_legacy_av_only",
+    "ep_stack_managed_detection_response",
+    "ep_stack_ngav_ml_based",
+    "ep_stack_no_protection",
+    "ep_stack_xdr_extended_detect"
+  ],
+  "numeric_features": [
+    "timestep",
+    "api_call_rate",
+    "registry_write_count",
+    "network_connection_count",
+    "process_injection_flag",
+    "c2_beacon_interval_sec",
+    "av_signature_hit_flag",
+    "sandbox_evasion_flag",
+    "lateral_propagation_count",
+    "privilege_escalation_flag",
+    "pe_entropy_mean",
+    "pe_entropy_std",
+    "import_hash_cluster",
+    "section_count",
+    "packed_section_ratio",
+    "string_entropy_mean",
+    "byte_histogram_chi2",
+    "code_section_rx_ratio",
+    "resource_section_entropy",
+    "suspicious_import_count",
+    "packer_detected_flag",
+    "api_burst_score",
+    "is_c2_active",
+    "is_high_net_volume",
+    "is_stealth_step",
+    "is_destructive_step",
+    "lateral_activity_score"
+  ],
+  "categorical_levels": {
+    "malware_family": [
+      "apt_implant",
+      "botnet_agent",
+      "cryptominer",
+      "dropper",
+      "fileless_malware",
+      "ransomware",
+      "rootkit",
+      "spyware",
+      "trojan",
+      "worm"
+    ],
+    "threat_actor_tier": [
+      "apt",
+      "commodity",
+      "crimeware",
+      "nation_state"
+    ],
+    "target_platform": [
+      "android_13",
+      "embedded_ot_firmware",
+      "linux_rhel_9",
+      "linux_ubuntu_22",
+      "macos_ventura",
+      "windows_10_enterprise",
+      "windows_11_pro",
+      "windows_server_2022"
+    ],
+    "obfuscation_technique": [
+      "anti_analysis_stall",
+      "code_signing_abuse",
+      "lotl_binary",
+      "packing",
+      "polymorphic_mutation",
+      "sandbox_evasion",
+      "string_encryption"
+    ],
+    "detection_outcome": [
+      "behavioural_flag",
+      "definitive_detection",
+      "heuristic_alert",
+      "sandbox_evasion_confirmed",
+      "signature_miss"
+    ],
+    "ep_stack": [
+      "av_plus_firewall",
+      "deception_honeypot",
+      "edr_endpoint_detect",
+      "legacy_av_only",
+      "managed_detection_response",
+      "ngav_ml_based",
+      "no_protection",
+      "xdr_extended_detect"
+    ]
+  },
+  "label_to_int": {
+    "c2_communication": 0,
+    "data_exfiltration": 1,
+    "dormancy_dwell": 2,
+    "initial_drop": 3,
+    "lateral_movement": 4,
+    "payload_execution": 5,
+    "persistence_establishment": 6,
+    "privilege_escalation": 7,
+    "sandbox_evasion_stall": 8,
+    "self_destruct_cleanup": 9
+  },
+  "int_to_label": {
+    "0": "c2_communication",
+    "1": "data_exfiltration",
+    "2": "dormancy_dwell",
+    "3": "initial_drop",
+    "4": "lateral_movement",
+    "5": "payload_execution",
+    "6": "persistence_establishment",
+    "7": "privilege_escalation",
+    "8": "sandbox_evasion_stall",
+    "9": "self_destruct_cleanup"
+  }
+}

feature_scaler.json

@@ -0,0 +1 @@
+{"mean": [29.5, 1.387591811594203, 2.5253623188405796, 4.403140096618357, 0.2543478260869565, 4.994391304347825, 0.29347826086956524, 0.34299516908212563, 0.03768115942028986, 0.08140096618357488, 0.8287420289855073, 0.18634782608695652, 274.6231884057971, 5.681159420289855, 0.42982463768115947, 0.5421188405797103, 41.10072463768116, 0.6250057971014492, 0.4523652173913043, 15.695652173913043, 0.463768115942029, 3.524582415458937, 0.11884057971014493, 0.33357487922705314, 0.45193236714975843, 0.0929951690821256, 0.3280193236714976, 0.13043478260869565, 0.13043478260869565, 0.13043478260869565, 0.07246376811594203, 0.057971014492753624, 0.08695652173913043, 0.08695652173913043, 0.13043478260869565, 0.08695652173913043, 0.08695652173913043, 0.21739130434782608, 0.3188405797101449, 0.42028985507246375, 0.043478260869565216, 0.08695652173913043, 0.057971014492753624, 0.07246376811594203, 0.11594202898550725, 0.057971014492753624, 0.3333333333333333, 0.13043478260869565, 0.14492753623188406, 0.14033816425120774, 0.14347826086956522, 0.1427536231884058, 0.14009661835748793, 0.15144927536231884, 0.14299516908212562, 0.1388888888888889, 0.0678743961352657, 0.17922705314009663, 0.08888888888888889, 0.10458937198067633, 0.5594202898550724, 0.11594202898550725, 0.11594202898550725, 0.08695652173913043, 0.15942028985507245, 0.14492753623188406, 0.15942028985507245, 0.13043478260869565, 0.08695652173913043], "std": [17.320194219715013, 0.13486579618110528, 2.8224558127303947, 3.855826464428149, 0.43554658867741924, 16.522749180589745, 0.45541065821011956, 0.4747672360871146, 0.22207359173815253, 0.2734829333055482, 0.13349684203848783, 0.0690646442535872, 164.83751594213814, 2.0467553940561625, 0.29063174139334635, 0.14071160667415852, 19.031317203687976, 0.16348965303394314, 0.17541357294450965, 5.309382618360122, 0.4987457613602604, 3.9756334300787786, 0.32363991799019004, 0.4715468040908369, 0.4977442571333736, 0.2904607481566321, 2.0197472660492055, 0.33682184196295206, 0.3368218419629521, 0.33682184196295206, 0.25928557483500797, 0.23371685876394413, 0.2818053712339797, 0.2818053712339797, 0.3368218419629521, 0.2818053712339797, 0.2818053712339797, 0.41252082351679387, 0.4660834006454619, 0.49366502689172936, 0.20395575381738024, 0.2818053712339797, 0.23371685876394416, 0.2592855748350079, 0.3201940649187907, 0.2337168587639441, 0.4714614640201808, 0.33682184196295206, 0.3520702854959198, 0.34737949256617373, 0.35060225443864834, 0.3498636771396811, 0.34712917169153373, 0.35852955456280744, 0.350110209377919, 0.34587231893054005, 0.25156062524270983, 0.3835886568811166, 0.2846176756328569, 0.30606055216695915, 0.4965166433941038, 0.3201940649187907, 0.32019406491879077, 0.2818053712339797, 0.3661117825566483, 0.3520702854959198, 0.36611178255664834, 0.3368218419629521, 0.2818053712339797]}

inference_example.ipynb

@@ -0,0 +1,314 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CYB003 Baseline Classifier — Inference Example\n",
+    "\n",
+    "End-to-end demo: load the trained XGBoost and PyTorch MLP models from the Hugging Face repo and predict the **malware execution phase** of a new per-timestep telemetry record.\n",
+    "\n",
+    "**Models predict one of 10 phases:** `c2_communication`, `data_exfiltration`, `dormancy_dwell`, `initial_drop`, `lateral_movement`, `payload_execution`, `persistence_establishment`, `privilege_escalation`, `sandbox_evasion_stall`, `self_destruct_cleanup`.\n",
+    "\n",
+    "**This is a baseline reference model**, not a production sandbox or EDR. See the model card for full metrics and limitations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Install dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --quiet xgboost torch safetensors pandas numpy huggingface_hub"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Download model artifacts from Hugging Face\n",
+    "\n",
+    "Five files are needed:\n",
+    "- `model_xgb.json` — XGBoost weights\n",
+    "- `model_mlp.safetensors` — PyTorch MLP weights\n",
+    "- `feature_engineering.py` — feature pipeline (must match training)\n",
+    "- `feature_meta.json` — feature column order + categorical levels\n",
+    "- `feature_scaler.json` — MLP input standardization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import hf_hub_download\n",
+    "\n",
+    "REPO_ID = \"xpertsystems/cyb003-baseline-classifier\"\n",
+    "\n",
+    "files = {}\n",
+    "for name in [\"model_xgb.json\", \"model_mlp.safetensors\",\n",
+    "             \"feature_engineering.py\", \"feature_meta.json\",\n",
+    "             \"feature_scaler.json\"]:\n",
+    "    files[name] = hf_hub_download(repo_id=REPO_ID, filename=name)\n",
+    "    print(f\"  downloaded: {name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys, os\n",
+    "fe_dir = os.path.dirname(files[\"feature_engineering.py\"])\n",
+    "if fe_dir not in sys.path:\n",
+    "    sys.path.insert(0, fe_dir)\n",
+    "\n",
+    "from feature_engineering import transform_single, load_meta, INT_TO_LABEL"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Load models and metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import xgboost as xgb\n",
+    "from safetensors.torch import load_file\n",
+    "\n",
+    "meta = load_meta(files[\"feature_meta.json\"])\n",
+    "with open(files[\"feature_scaler.json\"]) as f:\n",
+    "    scaler = json.load(f)\n",
+    "\n",
+    "N_FEATURES = len(meta[\"feature_names\"])\n",
+    "N_CLASSES = len(meta[\"int_to_label\"])\n",
+    "print(f\"feature count: {N_FEATURES}\")\n",
+    "print(f\"class count:   {N_CLASSES}\")\n",
+    "print(f\"label classes: {list(meta['int_to_label'].values())}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# XGBoost\n",
+    "xgb_model = xgb.XGBClassifier()\n",
+    "xgb_model.load_model(files[\"model_xgb.json\"])\n",
+    "\n",
+    "# MLP architecture (must match training)\n",
+    "class PhaseMLP(nn.Module):\n",
+    "    def __init__(self, n_features, n_classes=10, hidden1=128, hidden2=64, dropout=0.3):\n",
+    "        super().__init__()\n",
+    "        self.net = nn.Sequential(\n",
+    "            nn.Linear(n_features, hidden1),\n",
+    "            nn.BatchNorm1d(hidden1),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Dropout(dropout),\n",
+    "            nn.Linear(hidden1, hidden2),\n",
+    "            nn.BatchNorm1d(hidden2),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Dropout(dropout),\n",
+    "            nn.Linear(hidden2, n_classes),\n",
+    "        )\n",
+    "    def forward(self, x):\n",
+    "        return self.net(x)\n",
+    "\n",
+    "mlp_model = PhaseMLP(N_FEATURES, n_classes=N_CLASSES)\n",
+    "mlp_model.load_state_dict(load_file(files[\"model_mlp.safetensors\"]))\n",
+    "mlp_model.eval()\n",
+    "print(\"models loaded\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Prediction helper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MU = np.array(scaler[\"mean\"], dtype=np.float32)\n",
+    "SD = np.array(scaler[\"std\"],  dtype=np.float32)\n",
+    "\n",
+    "def predict_phase(record: dict) -> dict:\n",
+    "    \"\"\"Predict the execution phase for one per-timestep telemetry record.\n",
+    "\n",
+    "    Returns a dict with both models' predictions and per-class probabilities.\n",
+    "    \"\"\"\n",
+    "    X = transform_single(record, meta)\n",
+    "\n",
+    "    xgb_proba = xgb_model.predict_proba(X)[0]\n",
+    "    xgb_label = INT_TO_LABEL[int(np.argmax(xgb_proba))]\n",
+    "\n",
+    "    Xs = ((X - MU) / SD).astype(np.float32)\n",
+    "    with torch.no_grad():\n",
+    "        logits = mlp_model(torch.tensor(Xs))\n",
+    "        mlp_proba = torch.softmax(logits, dim=1).numpy()[0]\n",
+    "    mlp_label = INT_TO_LABEL[int(np.argmax(mlp_proba))]\n",
+    "\n",
+    "    return {\n",
+    "        \"xgboost\": {\n",
+    "            \"label\": xgb_label,\n",
+    "            \"probabilities\": {INT_TO_LABEL[i]: float(p) for i, p in enumerate(xgb_proba)},\n",
+    "        },\n",
+    "        \"mlp\": {\n",
+    "            \"label\": mlp_label,\n",
+    "            \"probabilities\": {INT_TO_LABEL[i]: float(p) for i, p in enumerate(mlp_proba)},\n",
+    "        },\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Run on an example record\n",
+    "\n",
+    "Real `lateral_movement` event lifted from the sample dataset: an APT-tier cryptominer at timestep 26 propagating laterally with 2 propagation events and 10 network connections. Both models should predict `lateral_movement`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real timestep record from the sample dataset (true phase: lateral_movement)\n",
+    "example_record = {\n",
+    "    \"timestep\": 26,\n",
+    "    \"malware_family\": \"cryptominer\",\n",
+    "    \"threat_actor_tier\": \"apt\",\n",
+    "    \"target_platform\": \"windows_10_enterprise\",\n",
+    "    \"obfuscation_technique\": \"code_signing_abuse\",\n",
+    "    \"api_call_rate\": 1.4167,\n",
+    "    \"registry_write_count\": 0,\n",
+    "    \"network_connection_count\": 10,\n",
+    "    \"process_injection_flag\": 1,\n",
+    "    \"c2_beacon_interval_sec\": 0.0,\n",
+    "    \"detection_outcome\": \"signature_miss\",\n",
+    "    \"av_signature_hit_flag\": 0,\n",
+    "    \"sandbox_evasion_flag\": 0,\n",
+    "    \"lateral_propagation_count\": 2,\n",
+    "    \"privilege_escalation_flag\": 0,\n",
+    "    \"ep_stack\": \"deception_honeypot\",\n",
+    "    \"pe_entropy_mean\": 0.8336,\n",
+    "    \"pe_entropy_std\": 0.25,\n",
+    "    \"import_hash_cluster\": 498,\n",
+    "    \"section_count\": 2,\n",
+    "    \"packed_section_ratio\": 0.7558,\n",
+    "    \"string_entropy_mean\": 0.5727,\n",
+    "    \"byte_histogram_chi2\": 45.52,\n",
+    "    \"code_section_rx_ratio\": 0.3628,\n",
+    "    \"resource_section_entropy\": 0.4418,\n",
+    "    \"suspicious_import_count\": 11,\n",
+    "    \"packer_detected_flag\": 1,\n",
+    "}\n",
+    "\n",
+    "result = predict_phase(example_record)\n",
+    "\n",
+    "print(f\"XGBoost  ->  {result['xgboost']['label']}\")\n",
+    "for lbl, p in sorted(result['xgboost']['probabilities'].items(), key=lambda x: -x[1])[:5]:\n",
+    "    print(f\"    P({lbl:30s}) = {p:.4f}\")\n",
+    "\n",
+    "print(f\"\\nMLP      ->  {result['mlp']['label']}\")\n",
+    "for lbl, p in sorted(result['mlp']['probabilities'].items(), key=lambda x: -x[1])[:5]:\n",
+    "    print(f\"    P({lbl:30s}) = {p:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: when the two models disagree\n",
+    "\n",
+    "XGBoost and the MLP can disagree on records far from the training-data manifold or in the three phases the baseline finds genuinely hard (`dormancy_dwell`, `sandbox_evasion_stall`, `self_destruct_cleanup`, each spanning the full timestep range). Disagreement is a useful signal: hand those cases to a human analyst or to a more expensive sequence-based detector."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Batch prediction on the sample dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import snapshot_download\n",
+    "import pandas as pd\n",
+    "\n",
+    "ds_path = snapshot_download(repo_id=\"xpertsystems/cyb003-sample\", repo_type=\"dataset\")\n",
+    "samples = pd.read_csv(f\"{ds_path}/malware_samples.csv\")\n",
+    "\n",
+    "# Score the first 200 timesteps\n",
+    "sample = samples.head(200).copy()\n",
+    "preds = [predict_phase(row.to_dict())[\"xgboost\"][\"label\"] for _, row in sample.iterrows()]\n",
+    "sample[\"xgb_pred\"] = preds\n",
+    "\n",
+    "ct = pd.crosstab(sample[\"execution_phase\"], sample[\"xgb_pred\"],\n",
+    "                 rownames=[\"true\"], colnames=[\"pred\"])\n",
+    "print(\"Confusion on first 200 sample rows (XGBoost):\")\n",
+    "print(ct)\n",
+    "acc = (sample[\"execution_phase\"] == sample[\"xgb_pred\"]).mean()\n",
+    "print(f\"\\nbatch accuracy on first 200 rows (in-distribution): {acc:.4f}\")\n",
+    "print(\"\\nNote: these rows include training-set samples. See validation_results.json\\n\"\n",
+    "      \"for proper held-out test metrics from disjoint samples.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Next steps\n",
+    "\n",
+    "- See `validation_results.json` for held-out test metrics (15 disjoint samples, 900 timesteps).\n",
+    "- See `multi_seed_results.json` for the across-10-seeds robustness picture (accuracy 0.905 ± 0.010).\n",
+    "- See `ablation_results.json` for per-feature-group contribution. `timestep` carries the dominant signal — kill chains progress in time, malware execution does too.\n",
+    "- The model card's **Limitations** section explains why `dormancy_dwell`, `sandbox_evasion_stall`, and `self_destruct_cleanup` are hard.\n",
+    "- For the full 280k-row CYB003 dataset and commercial licensing, contact **pradeep@xpertsystems.ai**."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

model_mlp.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5137ad720cf14877439db2fe50e5df589c6e2cbcc7598cc332548922bd5f8369
+size 75760

multi_seed_results.json

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+{
+  "purpose": "With n=100 samples and 10 classes, single-seed metrics carry test-fold variance. Multi-seed evaluation gives a more reliable performance picture.",
+  "seeds_evaluated": [
+    42,
+    7,
+    13,
+    17,
+    23,
+    31,
+    45,
+    99,
+    123,
+    200
+  ],
+  "per_seed": [
+    {
+      "seed": 42,
+      "test_n_classes": 10,
+      "accuracy": 0.9177777777777778,
+      "macro_f1": 0.7780699645112974,
+      "macro_roc_auc_ovr": 0.979171667321058
+    },
+    {
+      "seed": 7,
+      "test_n_classes": 10,
+      "accuracy": 0.8988888888888888,
+      "macro_f1": 0.7959031264581272,
+      "macro_roc_auc_ovr": 0.9762003477988086
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+      "seed": 13,
+      "test_n_classes": 10,
+      "accuracy": 0.9077777777777778,
+      "macro_f1": 0.7844193419282306,
+      "macro_roc_auc_ovr": 0.9756039083537456
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+      "test_n_classes": 10,
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+      "macro_f1": 0.7793567708150484,
+      "macro_roc_auc_ovr": 0.9725864270053698
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+    "accuracy_mean": 0.9046666666666667,
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+    "accuracy_min": 0.8822222222222222,
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+}

validation_results.json

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+{
+  "version": "1.0.0",
+  "dataset": "xpertsystems/cyb003-sample",
+  "task": "10-class execution_phase classification",
+  "baselines": {
+    "always_predict_majority_accuracy": 0.13666666666666666,
+    "majority_class": "initial_drop",
+    "random_guess_accuracy": 0.1
+  },
+  "split": {
+    "strategy": "group_aware (GroupShuffleSplit by sample_id, nested)",
+    "rationale": "100 unique malware samples generate 6,000 timesteps (60 per sample). Random row-split would leak per-sample correlations into the test fold. Group-aware split keeps train/val/test samples disjoint.",
+    "samples_train": 69,
+    "samples_val": 16,
+    "samples_test": 15,
+    "timesteps_train": 4140,
+    "timesteps_val": 960,
+    "timesteps_test": 900,
+    "seed": 42
+  },
+  "n_features": 69,
+  "label_classes": [
+    "c2_communication",
+    "data_exfiltration",
+    "dormancy_dwell",
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+    "lateral_movement",
+    "payload_execution",
+    "persistence_establishment",
+    "privilege_escalation",
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+    "self_destruct_cleanup"
+  ],
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+    "dormancy_dwell": 40,
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+  "models": {
+    "xgboost": {
+      "architecture": "Gradient-boosted decision trees, multi:softprob, 10 classes",
+      "framework": "xgboost",
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