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Anomalous Device Models

kql MEDIUM Azure-Sentinel
huntingmicrosoftofficial
This rule was pulled from an open-source repository and enriched with AI. Validate in a test environment before deploying to production.
View original rule at Azure-Sentinel →
Retrieved: 2026-05-08T23:00:01Z · Confidence: medium

Hunt Hypothesis

Adversaries may be using uncommon device models to evade detection and blend in with legitimate traffic. SOC teams should proactively hunt for this behavior in Azure Sentinel to identify potential compromise of endpoints or unauthorized device access.

KQL Query

// 
let AnomalousModelCount = 5;
DeviceInfo
| summarize arg_max(Timestamp, *) by DeviceId
| summarize ModelAppearanceCount=dcount(DeviceId) by Model
| where ModelAppearanceCount < AnomalousModelCount

Analytic Rule Definition

id: 9ec67c0b-e319-4f1c-bbea-67119d03740a
name: Anomalous Device Models
description: |
  This query finds anomalous models discovered
requiredDataConnectors:
- connectorId: MicrosoftThreatProtection
  dataTypes:
  - DeviceInfo
tactics: []
relevantTechniques: []
query: |
  // 
  let AnomalousModelCount = 5;
  DeviceInfo
  | summarize arg_max(Timestamp, *) by DeviceId
  | summarize ModelAppearanceCount=dcount(DeviceId) by Model
  | where ModelAppearanceCount < AnomalousModelCount

References

False Positive Guidance

Original source: https://github.com/Azure/Azure-Sentinel/blob/main/Hunting Queries/Microsoft 365 Defender/Device Inventory/Anomalous Device Models.yaml