Mastering KQL for Threat Hunting and Detection Engineering

By /

Listen to this Post

Featured Image

Introduction

Kusto Query Language (KQL) is a powerful tool for cybersecurity professionals, enabling efficient threat hunting, detection engineering, and incident response. With the rise of Microsoft Defender for Endpoint (MDE) and other security platforms, KQL has become essential for analyzing telemetry data and identifying malicious activity. This article explores key KQL techniques, including C2 beaconing detection, and provides actionable commands for security practitioners.

Learning Objectives

  • Understand how to leverage KQL for threat hunting in MDE.
  • Learn to detect C2 beaconing using aggregated report telemetry.
  • Apply practical KQL queries to enhance detection engineering workflows.

You Should Know

1. Detecting C2 Beaconing with MDE Telemetry

KQL Query:

DeviceNetworkEvents 
| where RemoteIP has_any ("malicious_ip1", "malicious_ip2") 
| summarize BeaconCount = count(), FirstSeen = min(Timestamp), LastSeen = max(Timestamp) by DeviceId, RemoteIP 
| where BeaconCount > 5

Step-by-Step Guide:

  1. This query filters network events for connections to known malicious IPs.
  2. It aggregates beaconing activity by device and remote IP, counting occurrences.
  3. Results with more than 5 connections indicate potential C2 beaconing.

2. Analyzing Process Execution Anomalies

KQL Query:

DeviceProcessEvents 
| where InitiatingProcessFileName endswith "powershell.exe" 
| where ProcessCommandLine contains "-nop -w hidden -e" 
| project Timestamp, DeviceName, ProcessCommandLine

Step-by-Step Guide:

1. Identifies PowerShell executions with suspicious command-line arguments.

  1. Filters for hidden execution (-w hidden) and no profile (-nop), common in malware.

3. Outputs timestamps and affected devices for investigation.

3. Hunting for Lateral Movement via RDP

KQL Query:

DeviceLogonEvents 
| where LogonType == "RemoteInteractive" 
| where AccountDomain != "CORPDOMAIN" 
| summarize LogonCount = count() by DeviceName, AccountName 
| where LogonCount > 3

Step-by-Step Guide:

1. Targets Remote Desktop Protocol (RDP) logons.

  1. Excludes corporate domain accounts to focus on external or suspicious logins.

3. Flags devices with multiple anomalous RDP sessions.

4. Identifying Data Exfiltration via DNS

KQL Query:

DeviceDnsEvents 
| where isnotempty(RemoteIP) 
| where QueryText has_any (".exe", ".dll", ".ps1") 
| summarize QueryCount = count() by DeviceName, QueryText

Step-by-Step Guide:

  1. Monitors DNS queries for executable or script-related strings.
  2. Highlights potential data exfiltration attempts via DNS tunneling.

3. Aggregates queries by device for prioritization.

5. Cloud Hardening: Detecting Unauthorized Azure Role Assignments

KQL Query:

AzureActivity 
| where OperationName == "Microsoft.Authorization/roleAssignments/write" 
| where Caller != "[email protected]" 
| project Timestamp, Caller, RoleDefinitionId

Step-by-Step Guide:

  1. Tracks Azure role assignment changes in the activity log.
  2. Filters out authorized admins to detect privilege escalation.

3. Outputs suspicious assignments for review.

What Undercode Say

  • Key Takeaway 1: KQL is indispensable for modern threat hunting, especially in MDE environments. Mastering queries like beaconing detection can significantly reduce dwell time.
  • Key Takeaway 2: Automation via KQL allows scalable detection engineering, but context matters—always correlate queries with other telemetry.

Analysis:

The integration of KQL into security operations empowers teams to proactively hunt threats and build robust detections. As adversaries evolve, continuous refinement of KQL queries—such as those for C2 beaconing or lateral movement—is critical. The future of detection engineering lies in combining KQL with machine learning and threat intelligence for faster, more accurate responses.

Prediction

KQL will become even more central to cybersecurity as Microsoft expands its security ecosystem. Expect deeper integrations with AI-driven analytics, enabling real-time anomaly detection and automated response workflows. Security teams must invest in KQL training to stay ahead of emerging threats.

For further learning, check out Mehmet E.’s blog and the Defender Ninja Show. Explore his course at Blu Raven Academy to deepen your KQL expertise.

IT/Security Reporter URL:

Reported By: Mehmetergene C2 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]

💬 Whatsapp | 💬 Telegram

📢 Follow UndercodeTesting & Stay Tuned:

𝕏 formerly Twitter 🐦 | @ Threads | 🔗 Linkedin

Related Posts: