622,923 attack attempts. From one IP address.

That is not a typo. I run a distributed honeypot network — six servers across different providers, all designed to look like vulnerable targets. Over the past several months, I have collected 5,486,152 attack events from 9,897 unique IPs.

This is not theoretical security research. This is raw data from real attackers doing real things to servers they thought were real. Every credential they tried. Every command they ran. Every piece of malware they tried to deploy.

Here is what 5.5 million attacks taught me.

What is a Honeypot?

A honeypot is a decoy server. It looks real — runs real services, accepts real connections, responds like a real system. But everything that happens gets logged. Every username, every password, every keystroke.

The key insight: no legitimate user ever connects to a honeypot. There is no reason to. So every single event I capture is malicious by definition. That is what makes honeypot data so valuable for training threat detection models — it is 100% labeled attack traffic.

I run six honeypots distributed across different cloud providers and regions. Every night, logs sync to my storage node. Every week, I retrain my ML models on fresh attack data.

The Scale of Automated Attacks

Let us talk about that 622,923 number.

One IP address hit my honeypots 622,923 times. The second-place attacker managed 405,124 attempts. Third place: 389,000+.

These are not humans. No person is sitting at a keyboard typing passwords 600,000 times. These are automated attack tools running 24/7 on compromised infrastructure, cycling through credential lists, scanning the entire internet for targets.

Here is what most people do not understand: the entire IPv4 address space gets scanned constantly. There are roughly 4.3 billion IPv4 addresses. Botnets can scan all of them in hours. The moment a new server comes online with an open port, it gets found.

I have watched fresh honeypots receive their first malicious connection within 4 minutes of going live. Four minutes. That is how fast you get targeted just by existing on the internet.

What Attackers Are Actually After

The most targeted username in my dataset: root with 421,384 attempts.

No surprise. Root is the holy grail — full system access. But the second most targeted username was interesting: sol with 61,740 attempts.

That is the default user for Solana validator nodes.

Attackers are not just spraying generic credentials anymore. They are specifically hunting crypto infrastructure — validator nodes, mining rigs, exchange servers. Where there is money, there are attackers.

The password list is even more revealing. One password — 3245gs5662d34 — was tried 29,174 times. That is not random. That is a default credential for some IoT device or router. Attackers maintain lists of known defaults and spray them everywhere.

What Happens After They Get In

I captured 446,654 post-exploitation commands. Here is what attackers actually do once they are inside:

First 10 seconds: Check who they are (whoami, id), check what system they are on (uname -a), check for other users (cat /etc/passwd).

Next 30 seconds: Download their toolkit. Usually wget or curl to pull a script from a command server. Sometimes they base64 encode the payload to avoid detection.

Under 60 seconds: Malware is running. Cryptominers, botnet agents, or ransomware. The entire compromise takes less than a minute.

5,369 attackers successfully logged in to my honeypots. Every single one followed this pattern. It is completely automated — no human decision-making involved.

Why Traditional Antivirus Fails

I submitted samples to VirusTotal. One piece of malware was missed by 76 detection engines. My ML model caught it.

Signature-based detection cannot keep up. Attackers recompile their malware constantly, changing just enough to evade signatures. Behavioral detection and machine learning are the only way to catch what is actually new.

How to Protect Yourself

1. Disable password authentication for SSH. Use keys only. This eliminates brute force entirely.

2. Change default credentials on everything. Routers, IoT devices, databases. If it shipped with a password, change it.

3. Monitor your logs. If you are seeing failed login attempts, you are being targeted. Most people never look.

4. Use threat intelligence. If an IP is attacking my honeypots today, it will attack your production server tomorrow. Block known bad actors before they try.

The internet is hostile by default. Every server is constantly probed. The question is not whether you will be targeted — it is whether you will notice.

I built Prometheus AI to handle this automatically. Real-time threat detection trained on millions of real attacks. If you want to see what is hitting your servers, try it free for 14 days.