---
license: mit
language:
- en
pretty_name: Advanced SIEM Dataset (TsFile)
tags:
- siem
- cybersecurity
- anomaly-detection
- security-events
- tsfile
- format:tsfile
- timeseries
task_categories:
- time-series-forecasting
- tabular-classification
size_categories:
- 100K<n<1M
---





# Advanced SIEM Dataset (TsFile)

This dataset is a **lossless conversion to the [Apache TsFile](https://tsfile.apache.org/)
format** of the HuggingFace dataset
[`darkknight25/Advanced_SIEM_Dataset`](https://huggingface.co/datasets/darkknight25/Advanced_SIEM_Dataset):
a synthetic SIEM (Security Information and Event Management) event log for
cybersecurity ML/AI research.

## Original dataset

- **Source dataset**: [darkknight25/Advanced_SIEM_Dataset](https://huggingface.co/datasets/darkknight25/Advanced_SIEM_Dataset)
- **Author / contact**: sunny thakur (sunny48445@gmail.com)
- **License**: MIT
- **Content**: 100,000 synthetic security events (JSON Lines) simulating SIEM logs
  across 8 event types — firewall, ids_alert, auth, endpoint, network, cloud, iot,
  ai — with MITRE ATT&CK techniques, threat-actor associations and unconventional
  IOCs. Intended for anomaly detection, threat classification, predictive analytics
  and UEBA.

## Scale

- **100,000** events, **43** columns after flattening (incl. Time)
- 8 event types (rows): ai 12,667 · endpoint 12,589 · auth 12,516 · cloud 12,511 ·
  ids_alert 12,500 · firewall 12,448 · iot 12,434 · network 12,335
- Time range: **2020-07-12 → 2030-07-10** (ISO 8601, second precision in source)

## TsFile storage mapping (table model)

| Role | Column(s) | Type |
|------|-----------|------|
| **TAG** | `event_type` | STRING — 8 types, one type = one device |
| **Time** | source `timestamp` | INT64 (ms) |
| **FIELD (DOUBLE)** | `meta_risk_score`, `meta_confidence`, `behav_baseline_deviation`, `behav_entropy` | DOUBLE |
| **FIELD (INT64)** | `process_id`, `src_port`, `dst_port`, `bytes`, `duration` | INT64 |
| **FIELD (BOOLEAN)** | `behav_frequency_anomaly`, `behav_sequence_anomaly` | BOOLEAN |
| **FIELD (STRING)** | `event_id`, `source`, `severity`, `description`, `raw_log`, `additional_info`, `action`, `user`, and all per-event-type fields (`src_ip`, `dst_ip`, `alert_type`, `signature_id`, `model_id`, `input_hash`, `device_id`, `cloud_service`, `method`, `mac_address`, …) plus `meta_geo_location`, `meta_device_hash`, `meta_user_agent`, `meta_session_id` | STRING |

## Conversion notes

- **Flattened to a single wide table**. In the source JSONL the per-event-type
  fields are already flat at the top level of each record, so they are carried over
  as columns as-is (each is sparse — populated only for its event type). The two
  nested dicts are flattened: `advanced_metadata` → `meta_*` (6 fields, 100% present)
  and `behavioral_analytics` → `behav_*` (4 fields, ~10% present).
- **No columns dropped, no rows dropped**: all 100,000 events (unique `event_id`)
  and all flattened fields are preserved. Sparse columns keep their nulls — TsFile
  simply does not write null cells.
- **Time**: source `timestamp` (ISO 8601) → INT64 epoch milliseconds. Rows are
  sorted ascending by `(event_type, Time)`.
- **Duplicate-timestamp handling (+1 ms)**: the source contains 35
  `(event_type, timestamp)` collisions at second precision — different events that
  happen to share the same second (a property of the source data, not introduced
  here). TsFile requires strictly increasing time within a device, so within each
  `event_type`, any row whose time is ≤ the previous row's is bumped to `prev + 1ms`.
  Exactly **35 rows** are nudged by one millisecond; **all 100,000 events are kept**.

## Layout

```
data/
└── advanced_siem_dataset.tsfile
```

## Usage

```python
from tsfile import TsFileReader

reader = TsFileReader("data/advanced_siem_dataset.tsfile")
schemas = reader.get_all_table_schemas()
tname = next(iter(schemas))

cols = ["event_type", "severity", "meta_risk_score", "src_ip", "alert_type"]
with reader.query_table(tname, cols, batch_size=65536) as rs:
    while (batch := rs.read_arrow_batch()) is not None:
        df = batch.to_pandas()
        # ... process ...
reader.close()
```

## Citation

```bibtex
@misc{advanced_siem_dataset,
  title  = {Advanced SIEM Dataset},
  author = {sunny thakur},
  url    = {https://huggingface.co/datasets/darkknight25/Advanced_SIEM_Dataset},
  note   = {Synthetic SIEM security-event dataset}
}
```

Original dataset licensed under MIT.