For decades, fraud and cybersecurity systems have leaned on IP blocklists to detect suspicious or malicious activity. But the truth is, these lists are increasingly ineffective, overly simplistic, and actively harmful to modern traffic intelligence. Here’s why — and how CandycornDB goes beyond blocklists to deliver high-resolution, real-time IP threat scoring.
Blocklists are inherently reactive. They rely on past reports or public contributions to flag IPs that were once used for spam, fraud, scraping, or anonymized traffic. But by the time an IP hits a blocklist:
Worse, blocklists don’t age well. IPs rotate frequently, especially for VPNs and mobile carriers. A blocklisted IP today may belong to a clean, legitimate user tomorrow. That leads to massive false positives, lost customers, and broken trust.
Instead of binary “blocked” or “not blocked,” CandycornDB uses layered analysis at the subnet and ASN (Autonomous System Number) level to understand the behavior of entire IP groups. This provides a deeper and more predictive model of fraud and abuse risk:
This model allows CandycornDB to detect emerging threats before they’re reported anywhere — and to avoid punishing legitimate users simply because they happen to share an IP block with a bad actor.
CandycornDB doesn’t just detect risk — it quantifies it. Every IP is scored using a proprietary blend of network behavior, threat intelligence, and live scanning signals. You get:
We built CandycornDB to replace the outdated “blocklist or bust” mindset with a smarter, layered approach. Whether you're filtering bots, scoring fraud, or monitoring traffic quality — the difference is night and day.
The internet has evolved. Attackers adapt faster than ever. Relying on static blocklists is like using a phonebook in 2025. It’s time for tools that learn, score, and react dynamically to shifting threats.
Start using CandycornDB today or sign up for early access to our fraud intelligence platform.