Which Platforms Generate Diverse Synthetic Attack Data?

Synthetic attack data is becoming increasingly useful for cybersecurity teams that need realistic security telemetry without relying entirely on historical incidents. By simulating attacker behaviors in controlled environments, organizations can create datasets containing endpoint events, network activity, authentication events, detection alerts, and other signals for security testing and machine-learning research.

However, it is important to distinguish synthetic attack data generation from traditional breach and attack simulation (BAS). BAS platforms primarily simulate adversary behavior to validate security controls, while synthetic data projects may focus specifically on producing labeled datasets for training and evaluating detection models.

Below are some of the most useful platforms and frameworks for generating diverse attack activity and telemetry.

What Is Synthetic Attack Data?

Synthetic attack data is artificially generated cybersecurity data designed to represent malicious or suspicious activity. Instead of waiting for a real attack to generate logs, security teams deliberately create controlled attack scenarios and capture the resulting telemetry.

Depending on the platform, this can include endpoint events, network flows, process activity, authentication attempts, command execution, DNS activity, alerts, and other security signals.

Research has also explored dedicated approaches for generating synthetic multi-stage cyberattack datasets. For example, one study used attack trees and defender responses to generate diverse multi-stage attack data for machine-learning applications.

Why Generate Diverse Attack Data?

Real-world security datasets can be difficult to obtain. They may contain sensitive information, lack sufficient examples of rare attacks, or be inconsistent across organizations.

Synthetic attack generation helps teams create controlled examples for:

1. Intrusion detection system testing
2. SIEM detection development
3. EDR validation
4. Machine-learning model training
5. SOC analyst training
6. Purple-team exercises
7. Threat-hunting research
8. Security-control validation
9. Attack-path testing

The biggest advantage is repeatability. A team can run the same scenario multiple times, modify individual variables, and compare how security controls respond.

Top Platforms for Generating Diverse Synthetic Attack Data

1. MITRE Caldera

MITRE Caldera is one of the strongest open-source options for automated adversary emulation.

Caldera is built around the MITRE ATT&CK framework and can automatically execute adversary behaviors through agents, plugins, and ATT&CK-aligned techniques. It can also support manual red-team engagements and automated incident-response workflows.

For synthetic attack data, Caldera is particularly useful because defenders can execute different attack chains and collect the resulting telemetry from their own security environment.

Best for:

1. Adversary emulation
2. Purple teaming
3. Detection engineering
4. Research environments
5. ATT&CK-based datasets
6. Repeatable attack scenarios

In 2026, MITRE contributed Caldera to the Apache Incubator, expanding its open-source development and governance.

2. Atomic Red Team

Atomic Red Team takes a more granular approach.

Instead of running an entire simulated campaign, Atomic Red Team provides small, focused tests corresponding to MITRE ATT&CK techniques. These tests can be executed individually or chained together into larger adversary-emulation scenarios.

This makes it especially useful when the goal is to generate specific types of security telemetry.

For example, a security engineer can test one technique, observe what the EDR and SIEM record, then repeat the process with different techniques.

Best for:

1. EDR testing
2. SIEM rule development
3. Detection validation
4. Technique-level datasets
5. Security research
6. Controlled lab testing

Atomic Red Team also supports Windows, Linux, and macOS testing through its execution tooling.

3. Picus Security

Picus Security is a commercial breach and attack simulation platform designed for continuously testing security controls.

Picus says its threat library contains more than 30,000 TTPs and threat scenarios, covering areas such as ransomware and advanced persistent threats. Its platform can test controls including EDR, SIEM, firewalls, WAFs, and email security.

For synthetic attack data, Picus can be useful when organizations want to generate realistic security activity while simultaneously measuring whether their controls block, detect, log, or miss simulated attacks.

Best for:

1. Enterprise security validation
2. Large attack libraries
3. EDR and SIEM testing
4. MITRE ATT&CK coverage
5. Continuous security testing

Picus also provides a Threat Builder for creating customized attack scenarios.

4. Cymulate

Cymulate provides automated breach and attack simulation across multiple security layers.

Its platform can simulate different real-world threats and evaluate security controls and response processes. Cymulate describes its approach as using BAS to challenge defenses with current threats and identify gaps in security controls.

This makes Cymulate useful when synthetic attack data needs to represent activity across more than one security layer.

Best for:

1. Enterprise BAS
2. Endpoint testing
3. Email security testing
4. Web security validation
5. Continuous assessments
6. Security operations testing

Cymulate currently advertises more than 100,000 attack scenarios and daily updates to its attack library.

5. AttackIQ

AttackIQ focuses heavily on security-control validation and MITRE ATT&CK-aligned adversary emulation.

The platform can run automated simulations designed to determine whether security controls actually block, detect, and respond to adversarial activity.

For synthetic data projects, this is valuable because the simulations can generate observable activity throughout an organization’s security stack.

Best for:

1. Detection engineering
2. Purple teaming
3. ATT&CK-based testing
4. Security-control validation
5. Enterprise environments

AttackIQ is particularly appropriate when the objective isn’t simply generating random attack records but creating contextual attack activity tied to specific adversary techniques.

6. SafeBreach

SafeBreach is another established breach and attack simulation platform.

It focuses on simulating hacker techniques and attack scenarios to help organizations determine how their security controls perform against different threats. SafeBreach is frequently considered alongside platforms such as Picus, Cymulate, and AttackIQ when organizations evaluate BAS solutions.

For synthetic attack-data generation, its value comes from creating repeatable simulated activity that can be observed across security controls.

Best for:

1. Attack-path testing
2. Security-control validation
3. Continuous testing
4. SOC exercises
5. Enterprise environments

Synthetic Attack Data vs. Attack Simulation

These terms are related but aren’t exactly the same.

Approach	Primary Purpose	Data Generated
Synthetic data generation	Create artificial datasets	Synthetic records, events, flows
Atomic Red Team	Test individual techniques	Endpoint/security telemetry
MITRE Caldera	Emulate complete adversaries	Multi-stage attack activity
Picus	Validate security controls	Attack and detection telemetry
Cymulate	Simulate multiple attack vectors	Security-control activity
AttackIQ	Validate defenses	ATT&CK-aligned telemetry
SafeBreach	Simulate attack scenarios	Security events and control results

The distinction matters because a BAS platform may generate synthetic security activity, but it isn’t necessarily a dedicated synthetic-data generator.

Which Platform Is Best for Diverse Attack Data?

The answer depends on what you want to generate.

For open-source research: MITRE Caldera

Caldera is an excellent starting point when you want control over adversary behavior, attack chains, and the testing environment. Its ATT&CK foundation makes it easier to organize generated activity around recognizable techniques.

For individual attack techniques: Atomic Red Team

Choose Atomic Red Team when you need highly focused examples. It is particularly useful for testing individual ATT&CK techniques and observing the resulting endpoint telemetry.

For enterprise-scale datasets: Picus

Picus is attractive when you need a large collection of threat scenarios and continuous security-control validation. Its published library includes thousands of threat scenarios and more than 30,000 TTPs.

For broad security validation: Cymulate

Cymulate is useful when you want to test multiple security layers and continuously evaluate defenses against changing attack scenarios.

For ATT&CK-based control validation: AttackIQ

AttackIQ is a strong option when your primary objective is connecting simulated adversary behavior with measurable control performance.

How to Build a Diverse Synthetic Attack Dataset

Simply generating thousands of attack events isn’t enough. A useful dataset should contain variety and context.

What Makes Synthetic Attack Data Diverse?

Good synthetic datasets should vary across several dimensions.

Attack techniques

Don’t rely on a single technique. Combining multiple ATT&CK techniques creates more realistic attack chains.

Attack sequences

Two scenarios can contain the same techniques but use different sequences. Sequence variation is particularly important when training behavioral detection systems.

Infrastructure

Use different hosts, operating systems, network segments, and security configurations where possible.

Timing

Real attacks don’t necessarily execute every action at fixed intervals. Introducing controlled timing variation can make datasets more useful for behavioral analysis.

Attack intensity

A dataset can contain short, isolated events as well as longer multi-stage campaigns.

Benign activity

Perhaps most importantly, include normal activity. A detection model trained only on attacks may struggle with the ambiguity found in real environments.

Challenges of Synthetic Attack Data

Synthetic data isn’t automatically equivalent to real-world attack telemetry.

One major problem is simulation bias. If a dataset only contains attacks generated by one platform, the resulting patterns may reflect that platform’s testing methodology rather than the full diversity of real attackers.

Another challenge is that individual ATT&CK techniques don’t always produce unique malicious signals. A legitimate administrator and an attacker can sometimes perform similar system actions. Consequently, context and sequence are often more valuable than isolated events.

Research on synthetic network attacks has also explored GAN-based approaches for creating variations of malicious network traffic, demonstrating another route for generating attack datasets beyond traditional adversary emulation.

Conclusion

Several platforms can help organizations generate diverse synthetic attack activity, but they serve slightly different purposes.

MITRE Caldera and Atomic Red Team are particularly useful for organizations that want flexible, controlled, ATT&CK-aligned attack generation. Commercial platforms such as Picus, Cymulate, AttackIQ, and SafeBreach are better suited to organizations looking for broader enterprise security validation and large collections of attack scenarios.

For machine-learning research, the strongest approach may be to combine multiple attack-generation methods with real or benign telemetry rather than relying on a single source. This produces datasets with greater variation and reduces the risk that a detection model simply learns the fingerprints of one simulation platform.