User Monitoring and Data Collection System
The user and entity activities are monitored for anomalies. Different behavioral factors are analyzed from generally visited websites to typically used applications.
Machine Learning System
A large amount of data generated by user monitoring systems is used and analyzed by machine learning system to detect anomalies.
Security Information and Event Management (SIEM) tools are also an option for teams looking to understand user behaviors.
The following are the key differences between UEBA and SIEM tools.
UEBA | SIEM |
---|---|
Operates in real-time.
|
Point-in-time analysis of event data.
|
Machine learning models and algorithms are used to analyze data in real time.
|
Manual effort is mostly required to analyze the data.
|
Provides risk scoring that rank the threats efficiently.
|
Alerts are generated based on events that may or may not be malicious.
|
Processes huge amounts of structured and unstructured data.
|
Processes structured logs only.
|
Risk Level | Timestamp | Result | User | Application | Access Device | Second Factor |
---|---|---|---|---|---|---|
High
Unusual Application Access The activity is: Normal Unnormal |
10:39 PM
January 04, 2021 |
DeniedEndpoint is not Trusted
|
Jmarty
|
Python Web SDK (kmb)
|
> iOS 14.4
|
Passcode
|
High
Unusual Application Access |
08:24 PM
January 04, 2021 |
DeniedEndpoint is not Trusted
|
Psmith
|
Microsoft OWA 166
|
> iOS 14.3
|
Passcode
|
Medium
Suspicious Endpoint |
06:05 PM
January 04, 2021 |
DeniedEndpoint is not Trusted
|
Bjoseph
|
Juniper SSL VPN 10
|
> iOS 13.5
|
Passcode
|
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