Add TsFile (converted from xpertsystems/cyb003-sample)

.gitattributes

@@ -58,3 +58,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+execution_events.tsfile filter=lfs diff=lfs merge=lfs -text
+malware_samples.tsfile filter=lfs diff=lfs merge=lfs -text
+sample_summary.tsfile filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- tabular-classification
+- time-series-forecasting
+tags:
+- tsfile
+- timeseries
+- cybersecurity
+- malware
+- malware-classification
+- threat-intelligence
+- apt
+- ransomware
+- synthetic-data
+- edr
+- sandbox-evasion
+- polymorphic
+- time-series
+- format:tsfile
+pretty_name: CYB003 — Synthetic Malware Behaviour & Classification (Sample) (TsFile)
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+  - split: environment_profiles
+    path: environment_profiles.tsfile
+  - split: execution_events
+    path: execution_events.tsfile
+  - split: malware_samples
+    path: malware_samples.tsfile
+  - split: sample_summary
+    path: sample_summary.tsfile
+---
+
+# CYB003 — Synthetic Malware Behaviour & Classification (Sample) (TsFile)
+
+Apache TsFile version of [`xpertsystems/cyb003-sample`](https://huggingface.co/datasets/xpertsystems/cyb003-sample).
+
+## Overview
+
+**CYB003 — Synthetic Malware Behaviour & Classification Dataset (Sample)** — This is a **free preview** of the full **CYB003 — Synthetic Malware Behaviour & Classification Dataset** product. It contains roughly **1 / 56th of the full dataset** at identical schema, family/tier distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.
+
+- **Tables:** `environment_profiles`, `execution_events`, `malware_samples`, `sample_summary`
+- **Rows:** 7,256 across 4 TsFile table(s).
+  - `environment_profiles`: 100 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
+  - `execution_events`: 1,056 rows — Time is the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
+  - `malware_samples`: 6,000 rows — Time is the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
+  - `sample_summary`: 100 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
+
+## Schema (TsFile structure)
+
+### `environment_profiles.tsfile`
+
+- **Time** (INT64, milliseconds) — synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
+- **TAG**: `profile_id` — device dimension(s); query one device with `WHERE profile_id=...`.
+- **FIELD** (11): `segment_id` (string), `endpoint_os` (string), `platform_type` (string), `ep_stack` (string), `patch_currency_score` (double), `user_privilege_level` (string), `network_segment_type` (string), `sandbox_presence_flag` (int64), `av_signature_age_days` (int64), `edr_behavioural_enabled` (int64), `network_egress_filtered` (int64)
+
+### `execution_events.tsfile`
+
+- **Time** (INT64, milliseconds) — the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
+- **TAG**: `sample_id`, `event_type` — device dimension(s); query one device with `WHERE sample_id=...`.
+- **FIELD** (8): `family_id` (string), `execution_phase` (string), `target_pid` (int64), `api_call_name` (string), `registry_key_path` (string), `file_path_mutated` (string), `network_dst_ip_class` (string), `dns_query_domain_class` (string)
+
+### `malware_samples.tsfile`
+
+- **Time** (INT64, milliseconds) — the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
+- **TAG**: `sample_id`, `malware_family` — device dimension(s); query one device with `WHERE sample_id=...`.
+- **FIELD** (28): `family_id` (string), `threat_actor_id` (string), `threat_actor_tier` (string), `target_platform` (string), `execution_phase` (string), `obfuscation_technique` (string), `api_call_rate` (double), `registry_write_count` (int64), `network_connection_count` (int64), `process_injection_flag` (int64), `c2_beacon_interval_sec` (double), `detection_outcome` (string), `av_signature_hit_flag` (int64), `sandbox_evasion_flag` (int64), `lateral_propagation_count` (int64), `privilege_escalation_flag` (int64), `ep_stack` (string), `pe_entropy_mean` (double), `pe_entropy_std` (double), `import_hash_cluster` (int64), `section_count` (int64), `packed_section_ratio` (double), `string_entropy_mean` (double), `byte_histogram_chi2` (double), `code_section_rx_ratio` (double), `resource_section_entropy` (double), `suspicious_import_count` (int64), `packer_detected_flag` (int64)
+
+### `sample_summary.tsfile`
+
+- **Time** (INT64, milliseconds) — synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
+- **TAG**: `sample_id` — device dimension(s); query one device with `WHERE sample_id=...`.
+- **FIELD** (28): `family_id` (string), `threat_actor_id` (string), `malware_family` (string), `threat_actor_tier` (string), `target_platform` (string), `campaign_attribution_cluster` (int64), `obfuscation_complexity_score` (double), `total_api_calls` (int64), `unique_api_calls` (int64), `registry_mutations_total` (int64), `files_mutated_count` (int64), `network_connections_total` (int64), `lateral_propagation_count` (int64), `c2_sessions_established` (int64), `execution_duration_steps` (int64), `av_detection_flag` (int64), `edr_detection_flag` (int64), `sandbox_evasion_success` (int64), `definitive_detection_flag` (int64), `privilege_escalated_flag` (int64), `data_exfiltrated_flag` (int64), `dwell_time_ratio` (double), `family_attribution_confidence` (double), `lotl_technique_count` (int64), `packer_detected_flag` (int64), `threat_actor_roi_score` (double), `coordinated_campaign_flag` (int64), `campaign_success_flag` (int64)
+
+## Conversion notes
+
+- Each CSV of the sample is converted into its own TsFile table (4 tables), so no table is merged with another and no column is dropped.
+- Event/telemetry tables use the source simulation clock as Time (INT64 milliseconds): `timestep` step counters and millisecond offsets are stored as-is, minute-based columns are multiplied by 60,000, and ISO/UTC strings are parsed to epoch milliseconds.
+- Static inventory/topology/summary tables have no per-row clock; their Time is a synthetic row index (0..N-1) over the stable source order.
+- Low-cardinality dimensions of each table (campaign/incident/asset/session ids, phases, labels) are declared as TAG columns; query one simulation with e.g. `WHERE campaign_id='...'`.
+
+## Usage
+
+Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
+
+```python
+from pathlib import Path
+from tsfile import TsFileReader
+
+path = Path("environment_profiles.tsfile")
+with TsFileReader(str(path)) as reader:
+    schemas = reader.get_all_table_schemas()
+    print("tables:", list(schemas))
+    table_name = next(iter(schemas))
+    table = schemas[table_name]
+    columns = [column.get_column_name() for column in table.get_columns()]
+    print("columns:", columns)
+    field_names = [
+        column.get_column_name()
+        for column in table.get_columns()
+        if column.get_column_name() not in {"Time", "time"}
+    ]
+    if field_names:
+        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
+            batch = result.read_arrow_batch()
+            if batch is not None:
+                print(batch.to_pandas().head())
+```
+
+## Source & license
+
+- Original dataset: [`xpertsystems/cyb003-sample`](https://huggingface.co/datasets/xpertsystems/cyb003-sample)
+- Source card title / publisher: CYB003 — Synthetic Malware Behaviour & Classification (Sample)
+- License: cc-by-nc-4.0 (as declared by the original dataset).

execution_events.tsfile

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94d771230d89621355f3b2264e42de80e2de81f5e5de8cb4aaf71a2c1dcc413c
+size 931296

malware_samples.tsfile

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4493f2c3e372379cf202af2441085e5e50453592873b57083f8a98bf1e09bf37
+size 538323

sample_summary.tsfile

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71c3a986d6f324980b1d51bf6d55abbcb34960c1a785b3ed9a380e140c2f9cf8
+size 417536

source_dataset_card.md

@@ -0,0 +1,235 @@
+---
+license: cc-by-nc-4.0
+task_categories:
+  - tabular-classification
+  - time-series-forecasting
+tags:
+  - cybersecurity
+  - malware
+  - malware-classification
+  - threat-intelligence
+  - apt
+  - ransomware
+  - synthetic-data
+  - edr
+  - sandbox-evasion
+  - polymorphic
+pretty_name: CYB003 — Synthetic Malware Behaviour & Classification (Sample)
+size_categories:
+  - 1K<n<10K
+---
+
+# CYB003 — Synthetic Malware Behaviour & Classification Dataset (Sample)
+
+**XpertSystems.ai Synthetic Data Platform · SKU: CYB003-SAMPLE · Version 1.0.0**
+
+This is a **free preview** of the full **CYB003 — Synthetic Malware Behaviour
+& Classification Dataset** product. It contains roughly **1 / 56th of the
+full dataset** at identical schema, family/tier distribution, and statistical
+fingerprint, so you can evaluate fit before licensing the full product.
+
+> 🤖 **Trained baseline available:**
+> [**xpertsystems/cyb003-baseline-classifier**](https://huggingface.co/xpertsystems/cyb003-baseline-classifier)
+> — XGBoost + PyTorch MLP for 10-class malware execution-phase prediction,
+> group-aware split by sample, multi-seed evaluation (accuracy 0.905 ± 0.010),
+> honest disclosure of which tasks need the full dataset.
+
+| File                       | Rows (sample) | Rows (full)   | Description                                    |
+|----------------------------|---------------|---------------|------------------------------------------------|
+| `environment_profiles.csv` | ~100            | ~3,200        | Endpoint environment configurations            |
+| `sample_summary.csv`       | ~100           | ~5,600        | Per-sample aggregate KPIs                      |
+| `execution_events.csv`     | ~1,056         | ~60,000       | Discrete malware lifecycle events              |
+| `malware_samples.csv`      | ~6,000         | ~280,000      | Per-timestep sample telemetry                  |
+
+## Dataset Summary
+
+CYB003 simulates malware execution lifecycles across endpoint protection
+stacks with calibrated detection/evasion outcomes, covering:
+
+- **9 malware families**: ransomware, trojan, rootkit, worm, spyware,
+  fileless_malware, cryptominer, botnet_agent, dropper
+- **4 threat-actor tiers**: commodity, crimeware, apt, nation_state — with
+  per-tier sandbox evasion budgets, LotL (Living-off-the-Land) abuse rates,
+  and polymorphic mutation probabilities
+- **Endpoint protection stacks**: legacy AV, NGAV (ML-based), EDR
+- **Static PE features**: entropy, packing detection, section anomalies,
+  import hash distributions
+- **Behavioural telemetry**: process injection, persistence mechanisms,
+  C2 beacon patterns, lateral spread
+- **Outcome modelling**: AV signature detection, EDR behavioural detection,
+  sandbox evasion success, family attribution confidence
+
+## Trained Baseline Available
+
+A working baseline classifier trained on this sample is published at
+**[xpertsystems/cyb003-baseline-classifier](https://huggingface.co/xpertsystems/cyb003-baseline-classifier)**.
+
+| Component | Detail |
+|---|---|
+| Task | 10-class malware execution-phase classification |
+| Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
+| Features | 69 (after one-hot encoding); pipeline included as `feature_engineering.py` |
+| Split | **Group-aware by sample_id** — train/val/test samples disjoint |
+| Validation | Single seed + multi-seed aggregate across 10 seeds |
+| Demo | `inference_example.ipynb` — end-to-end copy-paste |
+| Headline metrics | XGBoost: accuracy 0.905 ± 0.010, macro ROC-AUC 0.975 ± 0.002 (multi-seed) |
+
+The model card documents an honest finding worth knowing before licensing:
+**malware-family classification is at majority baseline on the sample's 100
+samples** (a sample-size constraint, not a method failure — the full
+280k-row dataset has ~5,600 samples and supports family classification
+properly). The baseline pivots to **execution-phase prediction**, which is
+strongly learnable on the sample data (91% accuracy, ROC-AUC 0.98, stable
+across 10 seeds) and is itself a real SOC use case for dynamic-analysis
+and EDR phase tagging.
+
+## Calibrated Benchmark Targets
+
+The full product is calibrated to 12 benchmark metrics drawn from
+authoritative threat intelligence and AV-testing sources (VirusTotal,
+AV-TEST, MITRE ATT&CK Evaluations, Mandiant M-Trends, CrowdStrike GTR,
+Verizon DBIR). The sample preserves the same calibration:
+
+| Test | Target | Observed | Verdict |
+|------|--------|----------|---------|
+| av_detection_rate_commodity | 0.6200 | 0.6319 | ✓ PASS |
+| edr_detection_rate_apt | 0.3100 | 0.3096 | ✓ PASS |
+| sandbox_evasion_rate_nation | 0.7200 | 0.7225 | ✓ PASS |
+| lateral_propagation_rate | 0.0950 | 0.1038 | ✓ PASS |
+| pe_entropy_mean_packed | 0.9100 | 0.9100 | ✓ PASS |
+| lotl_abuse_rate_apt | 0.4300 | 0.4300 | ✓ PASS |
+| dwell_time_ratio_apt | 0.3200 | 0.3198 | ✓ PASS |
+| family_attribution_confidence | 0.6800 | 0.6808 | ✓ PASS |
+| c2_detection_rate | 0.5400 | 0.5394 | ✓ PASS |
+| campaign_success_rate | 0.3400 | 0.2900 | ✓ PASS |
+| polymorphic_detection_penalty | 0.2400 | 0.2392 | ✓ PASS |
+| false_negative_rate_fileless | 0.3800 | 0.4203 | ✓ PASS |
+
+*Note: some benchmarks (e.g. campaign success rate, lateral propagation)
+require larger sample sizes to converge tightly. The full product passes
+all 12 benchmarks at Grade A- or better.*
+
+## Schema Highlights
+
+### `malware_samples.csv` (primary file, per-timestep telemetry)
+
+| Column                       | Type    | Description                                  |
+|------------------------------|---------|----------------------------------------------|
+| sample_id                    | string  | Unique malware sample identifier             |
+| family_id                    | string  | Malware family instance ID                   |
+| actor_id                     | string  | Threat actor ID                              |
+| timestep                     | int     | Step in malware lifecycle (0–59)             |
+| malware_family               | string  | 1 of 9 families                              |
+| threat_actor_tier            | string  | commodity / crimeware / apt / nation_state   |
+| target_platform              | string  | windows / linux / macos / android            |
+| ep_stack                     | string  | legacy_av / ngav_ml_based / edr_full         |
+| pe_entropy                   | float   | Portable Executable section entropy (0–1)    |
+| packer_detected_flag         | int     | Whether PE packer was detected               |
+| process_injection_count      | int     | Process-injection events at this step        |
+| persistence_mechanism        | string  | registry / scheduled_task / service / wmi    |
+| c2_beacon_active             | int     | Whether C2 channel is beaconing              |
+| sandbox_evaded               | int     | Whether sandbox evasion succeeded            |
+| av_detected                  | int     | AV signature detection at this step          |
+| edr_detected                 | int     | EDR behavioural detection at this step       |
+| dwell_time_hours             | float   | Cumulative dwell time                        |
+| lotl_technique_used          | string  | Living-off-the-Land binary if any            |
+
+### `sample_summary.csv` (per-sample outcome)
+
+| Column                            | Type    | Description                              |
+|-----------------------------------|---------|------------------------------------------|
+| sample_id, family_id, actor_id    | string  | Identifiers                              |
+| malware_family                    | string  | Family classification target             |
+| threat_actor_tier                 | string  | Tier classification target               |
+| target_platform                   | string  | Platform                                 |
+| campaign_success_flag             | int     | Boolean — successful campaign            |
+| av_detection_flag                 | int     | Boolean — AV detection ever              |
+| edr_detection_flag                | int     | Boolean — EDR detection ever             |
+| sandbox_evaded_flag               | int     | Boolean — sandbox evasion ever           |
+| packer_detected_flag              | int     | Boolean — packer detected                |
+| family_attribution_confidence     | float   | Confidence score (0–1)                   |
+| total_dwell_hours                 | float   | End-to-end dwell                         |
+| lateral_propagation_count         | int     | Count of lateral spread events           |
+
+See `execution_events.csv` and `environment_profiles.csv` for the discrete
+event log and endpoint environment schemas respectively.
+
+## Suggested Use Cases
+
+- Training **malware execution-phase classifiers** —
+  [worked example available](https://huggingface.co/xpertsystems/cyb003-baseline-classifier)
+- Training **malware family classifiers** (9-class with realistic class
+  imbalance and family-specific feature distributions — full dataset
+  recommended for adequate per-class sample size)
+- **Threat actor attribution** modelling (4-tier classification)
+- **EDR detection benchmarking** — packed vs unpacked, signature vs
+  behavioural, fileless vs binary
+- **Sandbox evasion detection** with tier-calibrated evasion budgets
+- **Polymorphic malware detection** — sample mutation effects on AV
+  signature coverage
+- **C2 beacon detection** with realistic beacon-active timestep patterns
+- **PE entropy / packing detection** — entropy distributions tied to
+  ground-truth packing flags
+- **Living-off-the-Land binary detection** for APT-tier samples
+
+## Loading the Data
+
+```python
+import pandas as pd
+
+samples    = pd.read_csv("malware_samples.csv")
+summaries  = pd.read_csv("sample_summary.csv")
+events     = pd.read_csv("execution_events.csv")
+environments = pd.read_csv("environment_profiles.csv")
+
+# Join per-timestep telemetry with per-sample summary labels
+enriched = samples.merge(summaries, on="sample_id", how="left",
+                         suffixes=("", "_summary"))
+
+# Family classification target
+y_family = summaries["malware_family"]
+
+# Threat-actor tier target
+y_tier = summaries["threat_actor_tier"]
+
+# Binary detection target (per-timestep)
+y_detected = (samples["av_detected"] | samples["edr_detected"]).astype(int)
+```
+
+For a worked end-to-end example with execution-phase classification,
+group-aware splitting, and feature engineering, see the inference notebook
+in the [baseline classifier repo](https://huggingface.co/xpertsystems/cyb003-baseline-classifier/blob/main/inference_example.ipynb).
+
+## License
+
+This **sample** is released under **CC-BY-NC-4.0** (free for non-commercial
+research and evaluation). The **full production dataset** is licensed
+commercially — contact XpertSystems.ai for licensing terms.
+
+## Full Product
+
+The full CYB003 dataset includes **~349,000 rows** across all four files,
+with calibrated benchmark validation against 12 metrics drawn from
+authoritative AV-testing and threat intelligence sources.
+
+📧  **pradeep@xpertsystems.ai**
+🌐  **https://xpertsystems.ai**
+
+## Citation
+
+```bibtex
+@dataset{xpertsystems_cyb003_sample_2026,
+  title  = {CYB003: Synthetic Malware Behaviour & Classification Dataset (Sample)},
+  author = {XpertSystems.ai},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/xpertsystems/cyb003-sample}
+}
+```
+
+## Generation Details
+
+- Generator version : 1.0.0
+- Random seed       : 42
+- Generated         : 2026-05-16 13:46:05 UTC
+- Lifecycle model   : Multi-timestep PE + behavioural + outcome simulation
+- Overall benchmark : 100.0 / 100  (grade A+)