Microsoft Sentinel - Hunting

Introduction

• Microsoft Sentinel Hunts, also known as “hunting queries,” refer to proactive search activities conducted by security analysts to identify potential threats or indicators of compromise (IoCs) within their environment.
• These hunts involve using custom queries to analyze data collected from various sources in Microsoft Sentinel to uncover suspicious activities that may not be detected by automated analytics rules.

Key Features of Microsoft Sentinel Hunts:

1. Proactive Threat Detection
   • Description: Hunting is a proactive approach where security analysts actively search for signs of malicious activity rather than waiting for alerts to be generated.
   • Purpose: Helps to identify advanced threats that may evade automated detection mechanisms.
2. Custom Queries
   • Description: Hunts use custom queries written in Kusto Query Language (KQL) to search through the data ingested into Microsoft Sentinel.
   • Flexibility: Allows analysts to tailor their searches to specific threat scenarios or indicators relevant to their environment.
3. Built-In Hunting Queries
   • Description: Microsoft Sentinel provides a library of built-in hunting queries that cover common threat scenarios and patterns.
   • Use Cases: Analysts can use these queries as-is or modify them to better suit their specific needs.
4. Threat Intelligence Integration
   • Description: Hunting queries can incorporate threat intelligence feeds to search for known IoCs such as IP addresses, domains, or file hashes associated with malicious activities.
   • Enhancement: Enriches hunting results with context from threat intelligence sources.
5. Interactive Analysis
   • Description: Hunts are interactive and iterative, allowing analysts to refine their queries based on initial findings and delve deeper into suspicious activities.
   • Exploration: Facilitates in-depth investigation and correlation of data across multiple sources.

Common Use Cases for Hunts in Microsoft Sentinel:

1. Suspicious Login Activities
   • Example Query: Identifying multiple failed login attempts followed by a successful login, which may indicate a brute-force attack.
   • Purpose: Detect potential account compromises or unauthorized access attempts.
2. Anomalous Network Traffic
   • Example Query: Searching for unusual outbound network connections to rare or suspicious domains.
   • Purpose: Identify potential data exfiltration or command-and-control (C2) communication.
3. Endpoint Behavior
   • Example Query: Detecting the execution of uncommon processes or commands on endpoints, which could indicate malware or unauthorized activities.
   • Purpose: Uncover malicious actions taken on endpoints within the network.
4. File Integrity Monitoring
   • Example Query: Monitoring changes to critical system files or configurations that should not frequently change.
   • Purpose: Identify potential tampering or unauthorized modifications.
5. User Behavior Analysis
   • Example Query: Looking for unusual patterns in user activity, such as access to sensitive data outside of normal working hours.
   • Purpose: Detect insider threats or compromised user accounts.

Benefits of Microsoft Sentinel Hunts

1. Enhanced Visibility: Provides deeper insights into potential threats that may not be captured by automated rules.
2. Improved Threat Detection: Helps to identify advanced and emerging threats through proactive exploration.
3. Customization: Allows security teams to tailor their threat hunting activities to their specific environment and threat landscape.
4. Knowledge Sharing: Built-in hunting queries and community contributions facilitate knowledge sharing and collaboration among security professionals.

Steps to Conduct Hunts in Microsoft Sentinel:

1. Define Objectives: Determine the goals of the hunt, such as identifying specific threat types or investigating unusual activities.
2. Create or Select Queries: Write custom KQL queries or use built-in hunting queries to search for the defined objectives.
3. Analyze Results: Review and analyze the query results to identify suspicious activities or patterns.
4. Refine Queries: Adjust and refine queries based on initial findings to explore deeper or different aspects of the data.
5. Document Findings: Record the findings and any detected threats for further investigation and response.
6. Take Action: If threats are identified, initiate appropriate incident response actions, such as isolating affected systems or blocking malicious activities.