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README.md

@@ -2,55 +2,62 @@
 license: cc-by-nc-4.0
 task_categories:
   - tabular-classification
-  - tabular-regression
 language:
   - en
 tags:
   - synthetic
   - test-data
-  - mock-data
-  - customer-data
+  - cybersecurity
+  - network-traffic
+  - intrusion-detection
+  - incident-response
   - tabular
-  - faker
 size_categories:
   - 10M<n<100M
-pretty_name: Free Synthetic Customer Data (50M)
+pretty_name: Free Synthetic Security Cascade (100M)
 ---
 
-# Free Synthetic Customer Data — 50M Rows
+# Free Synthetic Security Cascade — 100M Rows
 
-A free, fully synthetic dataset of **50,000,000 customer/user records**, generated for developers and builders who need realistic-looking test data without touching any real personal information.
+A free, fully synthetic dataset of 100,000,000 network flow records, generated for developers and builders working on intrusion detection, security analytics, and incident-response models — realistic network traffic without touching any real network, host, or personal data.
 
-Every value in this dataset is **artificially generated**. No real people, no scraped data, no real PII. It's built for seeding databases, load-testing APIs, prototyping apps, testing ETL pipelines, and demoing software with data that *looks* real but isn't.
+Every value in this dataset is artificially generated. No real captures, no scraped traffic, no real IPs or PII. What makes it different: most rows are ordinary background traffic, but malicious activity arrives in **cascades** — dense bursts of correlated events where one intrusion triggers a chain of follow-on activity, separated by genuine calm stretches. This mirrors how real incidents actually unfold, so a model trained here has to handle both the quiet and the storm, not just uniformly-sprinkled anomalies.
 
 ## Schema
 
 | Column | Type | Description |
 |---|---|---|
-| `full_name` | string | Synthetic first + last name |
-| `email` | string | Synthetic email address (name-derived) |
-| `street_address` | string | Synthetic street number + name |
-| `city` | string | Synthetic city name |
-| `state` | string | US state abbreviation (2-letter) |
-| `zip_code` | string | 5-digit US-style postal code |
-| `phone` | string | US-style phone number, `(NXX) NXX-XXXX` format |
-| `signup_date` | string | ISO date (`YYYY-MM-DD`), spread across ~6 years |
-| `account_status` | string | One of: `active`, `inactive`, `trial`, `suspended` |
-
-**Account status distribution (approx):** active 65%, inactive 15%, trial 12%, suspended 8%.
+| flow_id | string | Unique flow identifier |
+| timestamp | timestamp[ms] | Flow start time |
+| src_ip | string | Source IP address (synthetic) |
+| dst_ip | string | Destination IP address (synthetic) |
+| src_port | int32 | Source port |
+| dst_port | int32 | Destination port |
+| protocol | string | One of: TCP, UDP, ICMP |
+| bytes_sent | int64 | Bytes sent in the flow |
+| bytes_received | int64 | Bytes received in the flow |
+| duration_ms | int64 | Flow duration in milliseconds |
+| flag_pattern | string | TCP-style flag pattern (9 values, e.g. SYN-ACK-ACK, FIN-ACK, PSH-ACK) |
+| cascade_id | string | ID linking flows in the same attack cascade (null for normal traffic) |
+| cascade_type | string | Attack cascade type (null for normal traffic) |
+| event_severity | string | One of: none, low, medium, high, critical |
+| is_malicious_ground_truth | bool | Ground-truth label: true if the flow is part of an attack cascade |
+
+**Cascade types:** `port_scan_to_bruteforce`, `ddos_burst`, `exfiltration_pattern`, `credential_stuffing_wave`, `lateral_movement`.
+
+Normal background traffic has `cascade_id` and `cascade_type` null, `event_severity` of `none`, and `is_malicious_ground_truth` false. Malicious flows carry a shared `cascade_id` so you can group a full incident, a `cascade_type`, an elevated severity, and a true label.
 
 ## Format
 
-- **Apache Parquet**, Snappy compression
-- One file, ~2.8 GB, 50,000,000 rows
+- Apache Parquet, Snappy compression
+- One file, ~4.25 GB, 100,000,000 rows
 - Loads cleanly with pandas, polars, DuckDB, PyArrow, or the `datasets` library
 
 ## Quick start
 
 ```python
 import pandas as pd
-
-df = pd.read_parquet("synthetic_users_50M.parquet")
+df = pd.read_parquet("security_cascade_100M.parquet")
 print(df.head())
 ```
 
@@ -58,28 +65,27 @@ Or with the `datasets` library:
 
 ```python
 from datasets import load_dataset
-
-ds = load_dataset("ziadatalabs/FreeSyntheticCustomerData50M")
+ds = load_dataset("ziadatalabs/FreeSyntheticSecurityCascade100M")
 ```
 
 Or with DuckDB (great for querying without loading it all into memory):
 
 ```sql
-SELECT account_status, count(*)
-FROM 'synthetic_users_50M.parquet'
-GROUP BY account_status;
+SELECT cascade_type, count(*)
+FROM 'security_cascade_100M.parquet'
+WHERE is_malicious_ground_truth
+GROUP BY cascade_type;
 ```
 
 ## Notes
 
-- All data is synthetic and generated programmatically. Any resemblance to real individuals is coincidental.
-- Emails, addresses, and phone numbers follow realistic *formats* but are not real, deliverable, or dialable.
-- This is the first of several free synthetic datasets planned for builders — more types coming.
+- All data is synthetic and generated programmatically. Any resemblance to real hosts, IPs, or traffic is coincidental.
+- IPs, ports, and flow stats follow realistic formats and distributions but describe no real network.
+- The cascade clustering is **by design**: malicious events cluster into correlated bursts with quiet periods between them, so models trained on this data are stress-tested for real-world emergencies and incident response — ready for anything, not just evenly-spaced anomalies.
+- `is_malicious_ground_truth` and `cascade_id` give you clean labels for both per-flow classification and full-incident grouping.
 
 ## License & Usage
 
-Released under **CC BY-NC 4.0** — free for personal, research, and educational use, with attribution, **no commercial use**. See [the license](https://creativecommons.org/licenses/by-nc/4.0/) for details.
-
----
+Released under **CC BY-NC 4.0** — free for personal, research, and educational use, with attribution, no commercial use. See the license for details.
 
 *Published by Zia Data Labs. We create synthetic data — and we give some of it away free, because good test data shouldn't be hard to find.*