Behavior analysis
Effective behavior analysis processes use tools that analyze behavior on your network and use machine learning to find anomalies in behavior, which can notify you of potential threats. Where it could take a human days or weeks to find anomalies, machine learning algorithms can find this behavior in near real-time. You can do this by adopting a user and entity behavior analytics (UEBA) platform like Splunk User Behavior Analytics, which can seamlessly integrate with Splunk Enterprise Security. Augmenting your SIEM with UBA deepens your security capabilities by detecting and resolving use cases such as lateral movement, unknown threats, and data exfiltration.
Insider threat detection using Splunk User Behavior Analytics and Splunk Enterprise Security
Splunk User Behavior Analytics uses machine learning and your existing data in Splunk to find anomalies that may indicate malicious behavior, such as insider threat. Splunk User Behavior Analytics includes a variety of indicators for suspicious or unusual user behavior that can alert your security team to investigate further. Analysts no longer have to sort through mountains of data to find out what a particular user has been up to on the corporate network. They can go to the Splunk User Behavior Analytics dashboard to look up any user and see all their behavior across all systems and machines on the network.
Splunk User Behavior Analytics can also augment Splunk Enterprise Security to enhance workflow and simplify investigations by synchronizing threat management across both platforms.
What are the benefits of behavior analysis?
You can benefit from the use of behavior analysis in security analytics through automation and faster correlation of historical data. It would be impossible for any human analyst to pull together correlating events and try to simulate what the machine learning algorithms can do in a matter of seconds.
The algorithms in behavior analytics can lead to:
- Better anomaly detection
- Predictive analytics
- Clustering
What are behavior analysis best practices?
Gathering a baseline of normal activity for entities and users across the network provides you with a clear insight into anomalies when behavior falls out of the norm. This can be a leading indicator of a threat, or some other issue that may need further investigation.
How can Splunk Enterprise Security help with behavior analysis?
What behavior analysis processes can I put in place?
- Detecting cloud federated credential abuse in AWS
- This use case contains searches that detect abnormal processes that might indicate the extraction of federated directory objects.
- Detecting cloud federated credential abuse in Windows
- This use case contains searches that detect abnormal processes that might indicate the extraction of federated directory objects.
- Detecting insider threats
- User behavior monitoring uses machine learning and your existing data in Splunk to find anomalies that may indicate malicious behavior, such as insider threat.
- Detecting privilege escalation in your AWS environment
- These searches are designed to uncover potentially malicious events in your AWS environment.
- Detecting suspicious activities within AWS cloud instances
- These searches help you identify, respond to, and investigate suspicious activities in your cloud compute instances.
- Detecting unusual GCP service account usage
- How to use Splunk to monitor how GCP usage changes over time, and to set up alerting mechanisms that will notify the security team when unexpected access occurs.