Behavioral Anomaly Detection
Network intrusion detection that learns what normal looks like
For every host, dozens of behavioral facets, every hour of the day.
U.S. Patent 9,866,578
THE PROBLEM
Signatures can't catch what they've never seen
Firewalls, antivirus, and intrusion detection systems are essential. But they all work the same way — matching activity against known attack signatures. That works for known threats. It doesn't work for a zero-day exploit with no signature to match, an insider carrying valid credentials, or an APT that moves slowly enough to look like normal traffic.
A DIFFERENT APPROACH
You know your network better than any attacker does
Malicious activity almost always deviates from normal behavior. The hard part is seeing those deviations across an entire fleet, around the clock. Qato takes a different approach than signature-based tools. Instead of cataloging every possible attack, it learns what each host does normally — across dozens of behavioral facets, with separate baselines for different times of day. No two hosts share the same profile.
An attacker can study a public signature database and design around it. They can't study what Qato has learned about a specific server on your network.
HOW IT WORKS
From baseline to detection in four steps
01
Deploy
Lightweight agents install on Linux and Windows hosts. They collect behavioral statistics: connection counts, process activity, port usage, authentication events, and more. Results go to the Qato server. Minimal footprint, no kernel modules.
02
Learn
Qato builds rolling baselines for each host across dozens of behavioral facets. Separate profiles for business hours, off-hours, and weekends capture what's actually normal for that specific machine at that time of day. Baselines sharpen over weeks as more data accumulates.
03
Detect
When recent behavior deviates from baseline, Qato scores the anomaly. Each facet gets a deviation score weighted by importance. The result is an overall anomaly score with a full breakdown — which facets changed, by how much, and how that compares to the baseline. When anomalies cluster on the same host, Qato groups them into a detection. When anomalies appear across multiple hosts in the same time window, it flags a correlated cluster.
04
Investigate
Every anomaly links to captured evidence — the actual processes, connections, and events from that time window. Analysts see what happened, not just that something happened.
AlphaSix Qato is entering R&D testing with select customers.
Launching commercially in 2026. Contact our sales team for a demo today.