/// TELECOM · ANOMALY DETECTIONNetwork Anomaly Detection & Outage Prediction
Anomaly detection that learns what normal looks like for every node, turns alarm storms into single incidents, and catches degrading links before customers lose service.
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TRUSTED BY TEAMS
The problem in most network operations centres is not a lack of data but too much of it: one fault can set off thousands of alarms, and fixed thresholds either fire constantly or too late.
Sodio builds network anomaly detection for operators, ISPs and fibre networks that learns each node’s normal behaviour, groups related alarms into one incident with a likely cause, and spots slow degradation before it becomes an outage. It covers faults and performance; security detection stays with your security tools.
/// IS IT WORTH BUILDING YET?
When Anomaly Detection Is Worth Building
Large operators buy AIOps for their networks from their equipment vendors or dedicated platforms. For regional operators and ISPs, the question is whether alarm noise and late detection are costing more than the fix.
Worth building when
- Alarm volume overwhelms the operations team
- Outages are costly and visible to customers
- Past incidents and their causes have been recorded
- The network spans many vendors, sites and link types
Not yet, when
- A small network with few alarms that engineers handle easily
- Fixed thresholds in your monitoring tools already work well
- There is no history of incidents to learn from
- One vendor’s tools already cover the whole network
/// WHAT IT COVERS
What Network Anomaly Detection Covers
AI network monitoring is most useful when it reduces the work of the people watching the network, not when it adds another screen.
Dynamic baselines
Anomaly detection
Alarm correlation
Root cause suggestion
Degradation prediction
Cell site power and batteries
Weather-aware links
Customer impact
Incident summaries
Engineer feedback
/// WHAT IT WATCHES
What It Watches, and What It Catches
///FROM ALARM STORM TO INCIDENT
From an Alarm Storm to One Incident
When a fibre is cut, every router, switch and cell downstream reports its own failure. An engineer can face thousands of alarms that all describe one problem. Correlation uses the network's topology and the timing of each alarm to trace them back to the likely source, so the operations centre sees one incident with one probable cause.
Prediction works on the slower failures. An optical signal that weakens a little each week, a port whose errors are creeping up, or a cell site running on batteries after a power cut all give warning, if something is watching for it.
The measure of success is fewer, better alerts. If engineers receive more alarms after the system is switched on, it is not working.
The measure of success is fewer, better alerts. If engineers receive more alarms after the system is switched on, it is not working.
/// /// FAQ
Frequently Asked Questions
In network operations, it means spotting behaviour that is out of the ordinary for a particular part of the network — traffic, errors, latency, power or temperature — early enough to act before customers are affected. It works by learning what normal looks like for each node, rather than relying on fixed thresholds that are either too sensitive or too slow.
No. In cybersecurity, network anomaly detection means spotting intruders and attacks, and it is handled by dedicated network detection and response tools. This page is about faults and performance. Unusual traffic that looks like a security issue can be passed on to your security tools, but we do not replace them.
Some of them. Failures that build up slowly — optical signals weakening, error rates rising, a battery running down during a power cut, a link fading in heavy rain — can be seen coming. Sudden failures, such as a fibre cut by a digger, cannot be predicted, but they can be detected and traced to their cause within moments rather than after the phones start ringing.
By correlation. A single fibre cut can trigger thousands of alarms from every device downstream. The system uses the network’s topology and the timing of each alarm to group them into one incident with one likely cause, so engineers work on the problem instead of sorting through the noise.
No. It reads from the monitoring and management tools you already run and adds baselines, correlation and prediction on top. Any automatic response — rerouting traffic, for example — runs only under rules your engineers have approved.
By the numbers the network operations centre already cares about: how many alerts reach an engineer, how long it takes to detect a fault and find its cause, how many outages were caught early or missed, and how often the system raised a false alarm.
/// RELATED SERVICES
Related Industries and Services
Routes where scope overlaps with network anomaly detection.
/// GET STARTED
Start With Last Month’s Worst Outage
Share the alarms, logs and incident notes from a recent outage. We will assess, free, how much earlier it could have been seen, how many of those alarms could have been one incident, and how quickly the cause could have been found.