#FortiSIEM uses a proprietary Machine Learning algorithm that detects login anomalies by learning the user-to-workstation login patterns and forming dynamic peer user groups with similar login patterns. Users and workstations are represented using a bipartite graph. In a bipartite graph, the sets of nodes can be split into two disjoint sets, in such a way that there are no edges between the nodes within the same set. In this example, users and workstations form a bipartite graph, where the edge between a user and a workstation represents a login, and the edge weight represents the number of logins during a time interval.
Learn more about FortiSIEM: https://ftnt.net/6059Psg0Z
Explore the #Fortinet product demo center:
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For more information about Fortinet: https://ftnt.net/6052Psg0j
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Learn more about FortiSIEM: https://ftnt.net/6059Psg0Z
Explore the #Fortinet product demo center:
https://ftnt.net/6050Psg0w
More Fortinet demo videos:
https://ftnt.net/6051Psg0b
For more information about Fortinet: https://ftnt.net/6052Psg0j
Read our blog: https://ftnt.net/6053Psg0d
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