We can define the known attack scenarios, but how about the emerging and unknown threats in your environment? The volume of security events continues to grow, and the scope and sophistication of attacks are ever increasing. See the Supplemental Terms of Use for Microsoft Azure Previews for additional legal terms that apply to Azure features that are in beta, preview, or otherwise not yet released into general availability. Fusion-based detection for emerging threats is currently in PREVIEW.You can view and change the status of the rule, configure source signals to be included in the Fusion ML model, or exclude specific detection patterns that may not be applicable to your environment from Fusion detection. Configure Fusionįusion is enabled by default in Microsoft Sentinel, as an analytics rule called Advanced multistage attack detection.
Since Fusion correlates multiple signals from various products to detect advanced multistage attacks, successful Fusion detections are presented as Fusion incidents on the Microsoft Sentinel Incidents page and not as alerts, and are stored in the SecurityIncident table in Logs and not in the SecurityAlert table. By design, these incidents are low-volume, high-fidelity, and high-severity.Ĭustomized for your environment, this detection technology not only reduces false positive rates but can also detect attacks with limited or missing information. These incidents comprise two or more alerts or activities.
On the basis of these discoveries, Microsoft Sentinel generates incidents that would otherwise be difficult to catch. Microsoft Sentinel uses Fusion, a correlation engine based on scalable machine learning algorithms, to automatically detect multistage attacks (also known as advanced persistent threats or APT) by identifying combinations of anomalous behaviors and suspicious activities that are observed at various stages of the kill chain. For information about feature availability in US Government clouds, see the Microsoft Sentinel tables in Cloud feature availability for US Government customers.
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