Our operations team is drowning in alert noise from the monitoring module in cciot-24. We receive thousands of individual device alerts daily, but most are symptoms of upstream issues rather than root causes. We need better event correlation strategies to automatically group related events and identify the actual incident rather than flooding our team with correlated symptoms.
Current situation: a single network connectivity issue might generate 50+ device offline alerts, 100+ communication timeout alerts, and dozens of data gap alerts - all symptoms of the same root cause. Our team wastes hours manually correlating these events to understand the actual problem. We’ve experimented with simple time-window correlation (events within 5 minutes of each other), but this produces too many false positives and misses legitimate correlations across longer timeframes.
I’m interested in hearing about more sophisticated event correlation approaches. Are others using machine learning for pattern detection? How do you balance correlation sensitivity versus false positive rates? What’s worked for reducing alert noise while still catching real incidents?
We use a hybrid approach combining rule-based correlation for known patterns and anomaly detection for unknown patterns. Rule-based correlation handles common scenarios (network failures, power outages, scheduled maintenance) where the correlation logic is well-understood. Anomaly detection catches novel incident patterns we haven’t seen before. The rule-based system handles 80% of incidents with high accuracy, and anomaly detection catches the remaining 20% that would otherwise be missed.
The topology-aware correlation makes a lot of sense for our use case. We have a well-defined network hierarchy (gateways, switches, devices) that would map well to a correlation model. How do you handle updates to the topology? Our network configuration changes frequently as we add devices and restructure segments.
We sync the topology model with our network management database automatically. Whenever the network configuration changes, the topology model updates within minutes. The correlation engine uses the current topology state when evaluating events, so it always reflects the actual network structure. This requires integration between your monitoring system and network management tools, but it’s essential for accurate correlation as your infrastructure evolves.
A simpler approach that worked well for us is topology-aware correlation. Since you mentioned network connectivity issues causing cascading device alerts, build a network topology model into your correlation engine. When a gateway or network segment goes offline, the correlation engine knows which downstream devices are affected and automatically groups their alerts as symptoms of the upstream failure. This doesn’t require ML and dramatically reduces alert noise for infrastructure-related incidents.
For ML-based correlation, feature selection is more important than the algorithm choice. Focus on features that capture event relationships: temporal proximity (events within similar timeframes), spatial proximity (events from nearby devices or network segments), event type similarity (related event types like timeout and offline), and historical co-occurrence (events that have appeared together in past incidents). With good features, even simple algorithms like k-means work surprisingly well.