Event Analytics
Event Analytics runs many individual machine-learning jobs to detect real-time event anomalies from historical knowledge. What it finds is injected back as root-causal events through a machine-learning feedback loop — take the data, process it, analyze it, and alert it back into the real-time fault engine for correlation.
The point is not more alarms — it is fewer. Root-causal events drive noise suppression, so the operations floor sees the anomalies that are actionable and not the thousands that are not. CAPE policies then correlate and enrich those anomalies alongside the rest of the event stream.
Flow Analytics
Flow Analytics trains on historical flow data, then watches live traffic for the anomalies that matter.
Detects trouble in the services the network depends on — DHCP, DNS, LDAP, NTP, RADIUS — and failed session activity that points to something breaking.
Flags unusual traffic volumes across networks and interfaces — the ingress and egress patterns that precede congestion and degradation.
Surfaces brute-force attempts, rare activity, amplification-style floods, and reconnaissance scans — the traffic signatures of an attack in progress.
Detectors learn from roughly two months of history, then run in real time — anomalies flow through alerting into the event stream as actionable, de-duplicated events.
How AccuOSS delivers it
Machine learning is only as good as the data it learns from and the tuning behind it. AccuOSS trains and tunes the analytics against your history and wires the feedback loop into your operations.
Event Analytics jobs trained on your event history
Root-causal events wired into your fault correlation
Flow Analytics detectors tuned for your traffic
Noise-suppression thresholds set to your tolerance
A NOC that sees the actionable, not the ambient
AccuOSS trains Unified Assurance's analytics on your history and tunes the feedback loop for measurable noise reduction.