Internet threats don’t emerge overnight and neither does meaningful threat intelligence.

FraudGuard has been operating continuously for more than a decade now, and during that time we’ve done one thing exceptionally well: observe attackers at scale, refine our understanding of their behavior, and turn that knowledge into reliable, actionable intelligence for customers. The honeypot and Attack Correlation Engine (ACE) architecture that powers FraudGuard today is the result of years of iteration, tuning, and real-world validation, not a short-term research project or a recently assembled dataset.

At its core, FraudGuard operates what we believe to be one of the largest and most mature honeypot networks operating today—not just in terms of raw IP space, but in depth, longevity, and behavioral coverage.

Built for Longevity, Not Optics

From day one, FraudGuard was designed to look ordinary.

Our honeypot infrastructure spans vast amounts of routable IP space leased through long-standing datacenter partners, many of whom have been allocating address space for decades. These are not ephemeral, burstable cloud-only blocks that rotate every few weeks. They are stable, geographically diverse networks with realistic routing histories exactly the kind of infrastructure attackers already target.

This long-lived footprint matters. Attackers behave differently when they believe they are interacting with real infrastructure, and FraudGuard’s network is intentionally designed to blend into the background noise of the internet rather than announce itself as research tooling.

AI-Verified Traffic at Global Scale

Operating at this scale means not all traffic is worth keeping.

FraudGuard uses AI-assisted verification pipelines to analyze inbound traffic hitting our honeypot collection nodes. These systems help us differentiate between ambient internet noise, benign misconfiguration, and intentional hostile behavior allowing us to focus on traffic that demonstrates intent, persistence, and pattern.

This verification step is a critical reason FraudGuard data remains clean, consistent, and dependable for customers. Intelligence quality is not defined by how much you collect, it’s defined by how well you filter and contextualize it.

At peak operation, FraudGuard processes millions of discrete datapoints every day across this verification layer. AI-assisted analysis allows us to evaluate this volume continuously prioritizing high-signal activity while ensuring that short-lived anomalies and long-running attack campaigns are both captured accurately.

Expanding the Attack Surface

FraudGuard does not rely solely on passive observation.

Portions of our infrastructure are designed to understand how attackers discover and exploit exposed credentials and access artifacts in the wild. This includes carefully controlled, intentionally seeded API keys and service identifiers placed in public code repositories such as GitHub and GitLab. These artifacts are scoped to prevent real-world harm, while allowing us to observe attacker workflows once discovery occurs.

This approach provides insight into:

  • Credential harvesting automation
  • Replay behavior and timing
  • Tooling reuse across campaigns
  • Infrastructure shared between unrelated attacks

Importantly, FraudGuard operates this capability responsibly. Outbound interaction from our network is tightly controlled, legally compliant, and intentionally limited, ensuring that we never meaningfully participate in attacks or facilitate harm. The purpose is observation, attribution, and intelligence—not engagement.

ACE: Where History Matters

Raw attack data becomes exponentially more valuable when it’s connected over time.

FraudGuard’s Attack Correlation Engine (ACE) is the system that transforms isolated events into meaningful threat intelligence. ACE evaluates IP addresses across multiple dimensions: time, attack type, frequency, infrastructure reuse, cross-vector behavior, etc.

IPs that repeatedly appear across:

  • Different attack classes
  • Separate honeypot surfaces
  • Long time horizons
  • Coordinated activity clusters

naturally escalate in risk. This historical context is why FraudGuard customers consistently report lower false positives and higher confidence decisions. ACE doesn’t react to a single event; it recognizes patterns.

As a result, IPs within ACE may persist for vastly different lifespans. Some appear only briefly, active for a matter of hours before disappearing; while others demonstrate sustained, relentless behavior and remain visible across the system for months or even years. This temporal diversity is a core strength of ACE, allowing it to reflect the true lifecycle of modern attackers rather than forcing every signal into a fixed window.

A Small Window Into a Very Large System

Until recently, nearly all of this infrastructure operated behind the scenes. Recently, we’ve opened a tiny public window into the FraudGuard honeypot network and ACE processing pipeline.

Our ThreatView page provides a real-time visualization of live attack activity flowing through FraudGuard’s infrastructure. It’s a curated view but it reflects the same data sources, verification logic, and correlation principles that power our intelligence products.

What you see there represents only a fraction of what FraudGuard processes every day but it’s a glimpse into a system that’s been quietly observing, learning, and evolving for more than a decade.

If you’d like to explore this data firsthand, you can start with ThreatView or create a free FraudGuard.io trial to access our threat intelligence APIs. If you have questions along the way, reach us anytime at hello@fraudguard.io.