Query Details
id: c1d2e3f4-a5b6-4c7d-8e9f-0a1b2c3d4e6a
name: "Netskope - Multi-Protocol C2 Beaconing Pattern"
version: 1.0.0
kind: Scheduled
description: |
Detects regular-interval beaconing patterns to suspicious or low-reputation domains.
C2 implants often call back at fixed intervals. This rule identifies users with
high-frequency, evenly-spaced requests to domains with low CCL or suspicious categories.
Uses the custom NetskopeWebTx_CL table via Blob Storage ingestion.
MITRE ATT&CK: T1071 (Application Layer Protocol), T1573 (Encrypted Channel)
severity: High
requiredDataConnectors:
- connectorId: NetskopeWebTransactions
dataTypes:
- NetskopeWebTx_CL
queryFrequency: PT1H
queryPeriod: P1D
triggerOperator: gt
triggerThreshold: 0
tactics:
- CommandAndControl
relevantTechniques:
- T1071
- T1573
query: |
let _NetskopeEmpty = datatable(TimeGenerated:datetime, action_s:string, category_s:string, severity_s:string, malware_name_s:string, malware_type_s:string, threat_name_s:string, user_s:string, domain_s:string, dstip_s:string, srcip_s:string, bytes_uploaded_d:real, bytes_downloaded_d:real, app_s:string, url_s:string, dlp_rule_s:string, dlp_profile_s:string, activity_s:string, file_type_s:string, object_s:string, dst_country_s:string, src_country_s:string, ccl_s:string, access_method_s:string, traffic_type_s:string)[];
let SuspiciousCategories = dynamic([
"Uncategorized", "Unknown", "Newly Observed Domain",
"Newly Registered Domain", "Suspicious", "Parked",
"Dynamic DNS Host", "Malware", "Command and Control"]);
let RiskyCCL = dynamic(["poor", "low", "unknown"]);
union isfuzzy=true _NetskopeEmpty, NetskopeWebTx_CL
| where TimeGenerated > ago(1d)
| where isnotempty(user_s) and isnotempty(domain_s)
| where category_s in (SuspiciousCategories) or ccl_s in (RiskyCCL)
| summarize
RequestCount = count(),
RequestTimes = make_list(TimeGenerated, 200),
TotalBytesSent = sum(todouble(bytes_uploaded_d)),
TotalBytesRecv = sum(todouble(bytes_downloaded_d)),
SourceIPs = make_set(srcip_s, 5),
Categories = make_set(category_s, 5),
CCL = take_any(ccl_s),
DstIPs = make_set(dstip_s, 5),
DstCountries = make_set(dst_country_s, 5),
FirstSeen = min(TimeGenerated),
LastSeen = max(TimeGenerated)
by user_s, domain_s
| where RequestCount >= 20
| extend
DurationMinutes = datetime_diff('minute', LastSeen, FirstSeen),
AvgIntervalSec = iff(RequestCount > 1,
round(toreal(datetime_diff('second', LastSeen, FirstSeen)) / (RequestCount - 1), 1),
0.0)
| where DurationMinutes >= 60
| where AvgIntervalSec between (10.0 .. 600.0)
| project
user_s, domain_s, RequestCount,
AvgIntervalSec, DurationMinutes,
TotalBytesSent, TotalBytesRecv,
Categories, CCL, DstIPs, DstCountries, SourceIPs,
FirstSeen, LastSeen
| order by RequestCount desc, AvgIntervalSec asc
entityMappings:
- entityType: Account
fieldMappings:
- identifier: FullName
columnName: user_s
- entityType: DNS
fieldMappings:
- identifier: DomainName
columnName: domain_s
customDetails:
RequestCount: RequestCount
AvgIntervalSec: AvgIntervalSec
CCL: CCL
alertDetailsOverride:
alertDisplayNameFormat: "Netskope C2 Beaconing - {{user_s}} → {{domain_s}} ({{AvgIntervalSec}}s interval)"
alertDescriptionFormat: "User {{user_s}} shows beaconing pattern to {{domain_s}} with {{RequestCount}} requests at ~{{AvgIntervalSec}}s intervals."
incidentConfiguration:
createIncident: true
groupingConfiguration:
enabled: true
reopenClosedIncident: false
lookbackDuration: PT6H
matchingMethod: Selected
groupByEntities:
- Account
- DNS
groupByAlertDetails: []
groupByCustomDetails: []
This query is designed to detect suspicious network activity that may indicate a Command and Control (C2) beaconing pattern. Here's a simple breakdown of what it does:
Purpose: It identifies users who are making frequent, evenly-timed requests to domains that are either suspicious or have a low reputation. This can be a sign of malware communicating with a C2 server.
Data Source: The query uses data from the NetskopeWebTx_CL table, which is ingested via Blob Storage.
Detection Criteria:
Output: The query outputs details such as the user, domain, request count, average interval, total bytes sent and received, and other relevant information.
Alerting: If the criteria are met, an alert is generated with details about the user and domain involved, including the average interval of requests.
Incident Management: The query is configured to create incidents for detected patterns, with options for grouping related alerts by user account and domain.
Severity and Techniques: The severity of the alert is high, and it maps to MITRE ATT&CK techniques T1071 (Application Layer Protocol) and T1573 (Encrypted Channel), indicating potential use of application layer protocols and encrypted channels for C2 communication.
In summary, this query helps identify potential C2 communication by detecting regular, suspicious network activity patterns, which can be crucial for early detection of cyber threats.

David Alonso
Released: May 14, 2026
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