Malware Detections by Detection technology Trend

Description

This query visualises total emails with Malware detections over time summarizing the data daily by various Malware detection technologies/controls.

Query · kql

let TimeStart = startofday(ago(30d));
let TimeEnd = startofday(now());
let baseQuery = EmailEvents
| where Timestamp >= TimeStart
| where DetectionMethods has "Malware";
let av=baseQuery
| project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
| where Malware has 'Antimalware engine'
| make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
| extend Details = "Antimalware engine";
let fd=baseQuery
| project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
| where Malware has 'File detonation' and Malware !has 'File detonation reputation'
| make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
| extend Details = "File detonation";
let fdr=baseQuery
| project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
| where Malware has 'File detonation reputation'
| make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
| extend Details = "File detonation reputation";
let ud=baseQuery
| project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
| where Malware has 'URL detonation' and Malware !has 'URL detonation reputation'
| make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
| extend Details = "URL detonation";
let udr=baseQuery
| project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
| where Malware has 'URL detonation reputation' 
| make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
| extend Details = "URL detonation reputation";
let umr=baseQuery
| project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
| where Malware has 'URL malicious reputation' 
| make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
| extend Details = "URL malicious reputation";
union av,fd,fdr,ud,udr,umr
| project Count, Details, Timestamp
| render timechart
Raw source Malware Detections by Detection technology Trend · KQL
Esc
Published by Azure/Azure-Sentinel ↗, licensed under MIT ↗. Reproduced here unmodified. 
id: 14f54d33-81dd-4316-a617-2262cac86f37
name: Malware Detections by Detection technology Trend
description: |
  This query visualises total emails with Malware detections over time summarizing the data daily by various Malware detection technologies/controls.
description-detailed: |
  This query visualises total emails with Malware detections over time summarizing the data daily by various Malware detection technologies/controls in Microsoft Defender for Office 365.
  Query is also included as part of the Defender for Office 365 solution in Sentinel: https://techcommunity.microsoft.com/blog/microsoftdefenderforoffice365blog/part-2-build-custom-email-security-reports-and-dashboards-with-workbooks-in-micr/4411303
requiredDataConnectors:
- connectorId: MicrosoftThreatProtection
  dataTypes:
  - EmailEvents
tactics:
  - InitialAccess
relevantTechniques:
  - T1566
query: |
  let TimeStart = startofday(ago(30d));
  let TimeEnd = startofday(now());
  let baseQuery = EmailEvents
  | where Timestamp >= TimeStart
  | where DetectionMethods has "Malware";
  let av=baseQuery
  | project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
  | where Malware has 'Antimalware engine'
  | make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
  | extend Details = "Antimalware engine";
  let fd=baseQuery
  | project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
  | where Malware has 'File detonation' and Malware !has 'File detonation reputation'
  | make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
  | extend Details = "File detonation";
  let fdr=baseQuery
  | project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
  | where Malware has 'File detonation reputation'
  | make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
  | extend Details = "File detonation reputation";
  let ud=baseQuery
  | project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
  | where Malware has 'URL detonation' and Malware !has 'URL detonation reputation'
  | make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
  | extend Details = "URL detonation";
  let udr=baseQuery
  | project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
  | where Malware has 'URL detonation reputation' 
  | make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
  | extend Details = "URL detonation reputation";
  let umr=baseQuery
  | project Timestamp,RecipientEmailAddress,NetworkMessageId, DT=parse_json(DetectionMethods) | evaluate bag_unpack(DT) 
  | where Malware has 'URL malicious reputation' 
  | make-series Count= count() default = 0 on Timestamp from TimeStart to TimeEnd step 1d 
  | extend Details = "URL malicious reputation";
  union av,fd,fdr,ud,udr,umr
  | project Count, Details, Timestamp
  | render timechart
version: 1.0.0

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