Clustering
Anomaly Detection finds anomalies in Wide-Area Network (WAN) traffic patterns. Â
Highly-scalable solution for analyzing network data
Advanced machine learning algorithms for automated classification and clustering of network devices
Detect anomalies in highly-detailed flow data from existing network devices
Clustering offers top of the line network analysis
SightLab’s Clustering module is an extension to our Anomaly Detection module. Whereas Anomaly Detection requires pre-classification of the input data, Clustering will perform automated classification based on the similarities in the interactions described in the data. Anomaly models are then built for all traffic within and between clusters. This allows Clustering to scale up to large, complex data sources without requiring extensive data preparation. Our main implementation uses network flow information collected from Local Area Networks, Wide-Area Networks or data centre environments and is very well suited for detecting discovery techniques and lateral movement.
Use existing network devices as a data source
Detect the undetected
Highly scalable and cost-effective cyber security solution
Clustering is intertwined with our overall platform
Clustering finds anomalies in unclassified netflow data. This forms part of our overall platform strategy: using existing data sources to detect adversary behaviour. By using existing data sources, a holistic view is obtained. No more blindspots in your IT landscape! This strongly improves the chance of detecting the undetected: the advanced hackers that have passed your defences and are preparing to severely impact your organization. Do not allow hackers to hide on your own turf.
