README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
language:
  - en
tags:
  - synthetic
  - test-data
  - cybersecurity
  - network-traffic
  - intrusion-detection
  - incident-response
  - tabular
size_categories:
  - 10M<n<100M
pretty_name: Free Synthetic Security Cascade (100M)

Free Synthetic Security Cascade — 100M Rows

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 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
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, ~4.25 GB, 100,000,000 rows
• Loads cleanly with pandas, polars, DuckDB, PyArrow, or the datasets library

Quick start

import pandas as pd
df = pd.read_parquet("security_cascade_100M.parquet")
print(df.head())

Or with the datasets library:

from datasets import load_dataset
ds = load_dataset("ziadatalabs/FreeSyntheticSecurityCascade100M")

Or with DuckDB (great for querying without loading it all into memory):

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 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 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.