Why IP Blocklists Don't Work
(And What Does)

For decades, fraud and cybersecurity systems have leaned on IP blocklists to detect suspicious activity. It's the standard playbook: download a list of "bad IPs" from a public repo, load it into your firewall, and block anything that matches.

But the truth is, these lists are increasingly ineffective. In 2025, static blocklists are overly simplistic, slow to update, and actively harmful to your user experience. Here is why the old method is failing—and how Subnet Intelligence is replacing it.

The Fatal Flaw of Static Lists

Blocklists are inherently reactive. They rely on past reports or manual submissions to flag an IP that was used for spam or fraud. But by the time an IP hits a public blocklist:

• The attacker has already moved on to a new IP.
• Legitimate users might have inherited the "dirty" IP (collateral damage).
• There is zero context on why it was blocked.

Subnet Intelligence: The Modern Solution

Instead of binary "blocked / not blocked" checks, CandycornDB uses layered analysis at the Subnet and ASN (Autonomous System Number) level.

We don't just look at the single IP address hitting your server. We look at the neighborhood it comes from. This provides a predictive model of risk:

• ASN Scoring: We analyze how the entire network provider behaves. Is it a known "Bulletproof Host" that ignores abuse reports? Or is it a strict residential ISP?
• Subnet Clustering: If we see fraud coming from 192.168.1.5 and 192.168.1.10, our engine automatically flags the entire 192.168.1.0/24 range as suspicious.
• Real-Time Signals: Our system continuously ingests behavioral data from anonymizers, scanning patterns, and honeypots.

What Makes CandycornDB Different?

We don't just detect risk — we quantify it.

Every IP query returns a calculated risk score (0–100) based on a proprietary blend of network behavior, threat intelligence, and live scanning signals. This allows you to set nuanced rules:

// Instead of "Block if in list":
if (score > 85) {
  blockUser();
} else if (score > 60) {
  require2FA(); // Add friction, don't ban
}

This granular approach saves your conversion rates. You stop the bots, but you let the slightly suspicious (but legitimate) users prove they are human.

Final Thoughts

The internet has evolved. Attackers adapt faster than ever. Relying on static blocklists is like using a phonebook in 2025 to screen calls. It’s time for tools that learn, score, and react dynamically to shifting threats.