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Splunk uba documentation9/24/2023 ![]() Perform regular maintenance of your Splunk UBA deployment by managing the number of threats and anomalies in your system. Threat revalidation can take a long time and cause memory issues on your system depending on a variety of factors, including the types and age of the anomalies involved in the threat, the number or anomalies and entities involved in the threat, and any custom threat rules active in the system. The Offline Rule Executor in Splunk UBA runs nightly to process the scheduled anomaly and threat rules, and also performs threat revalidation in real time when there are rule changes, anomalies are removed from the system, or anomaly scores are changed. The following image shows the location of a user with an unusually high volume of data leaving the network.Manage the number of threats and anomalies in your environment The following image shows the daily and weekly baseline values for outgoing bytes compared to the spike shown in green of daily and weekly unusually high outgoing bytes: The following image shows an example of an abnormally high amount of outgoing bytes for a device. Here, the user Claude Shannon has an abnormally high volume of bytes leaving the network compared to the weekly and daily baselines created for this user. The following image shows how the alert appears when there is a high volume of outgoing bytes for a user. The purpose of this connection profiling is to reduce instances of false positives (FP). This model only considers connections that behave as file transfers while ignoring other types of connections like regular web browsing and interactive chat and video connections. Detects outliers in the time series of outgoing bytes transmission by each device after profiling network traffic connection. This model uses network traffic profiling. Unusual Volume of Data Uploaded per Device ModelÄetects outliers in the timeseries of outgoing bytes transmission per internal device. This profiling feature is only applicable when network events provide information about the number of packets involved. This model only considers connections that behave as file transfers while ignoring other types of connections like regular web browsing and interactive chat/video connections. Detects outliers in the time series of outgoing bytes transmission by each user after profiling network traffic connection. Unusual Volume of Data Uploaded per User ModelÄetects outliers in the timeseries of outgoing bytes transmission by each user. Splunk UBA version 5.3.0 includes the following four time-series batch models for data exfiltration detection: The following image shows how you can check to see if the anomalies are related to any of the detected threats on the Data Exfiltration by Suspicious Data Transfer panel: The following image shows a chart you can use to see a comparison of daily volume with the average on daily usage: The following image shows how the time-series model might trigger Excessive Data Transmission anomalies to show in your Latest Anomalies paneI: ![]() Performance scalability significantly improved for large-scale deployments.The interpretation of anomalies from the UI displays additional details.Models now handle different data transferring modes.Time-series batch models in Splunk UBA version 5.3.0 include the following enhancements: The model runs over 30 days, and mines the feature fields in the semiaggr_s cube to detect unusual data transmissions. The amount of outgoing bytes must be greater than an absolute threshold, and if peer groups exist, then there is also a peer group baseline and threshold ratio for the peer groups. The model flags if the amount of outgoing bytes is greater than the baseline gaussian mean, by a ratio of threshold times the mean. The model creates a baseline per user or per device for a daily and weekly window. Time-series models are created on a user or a device level, and can also be created over peer groups if peer groups exist. Time-series models can detect a large amount of data leaving an internal source entity, that is going out to an external destination. ![]()
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