🎯 Lesson 1: Threat Hunting Methodologies
Understanding different approaches to proactive threat hunting
📚 Learning Objectives
By the end of this lesson, you will be able to:
- Understand different threat hunting methodologies and their applications
- Develop hypothesis-driven hunting strategies
- Implement data-driven hunting approaches
- Apply threat intelligence-driven hunting techniques
- Assess hunting maturity and capabilities
- Choose appropriate methodologies for different scenarios
🔍 Introduction to Threat Hunting
What is Threat Hunting?
Threat hunting is a proactive cybersecurity practice where security professionals actively search for threats, vulnerabilities, and malicious activities that may have evaded traditional security controls. Unlike reactive security measures, threat hunting assumes that adversaries are already present in the environment.
🔑 Key Characteristics of Threat Hunting:
- Proactive: Searches for threats before they cause damage
- Hypothesis-driven: Based on educated assumptions about attacker behavior
- Iterative: Continuous process of refinement and improvement
- Data-intensive: Relies on comprehensive data collection and analysis
Threat Hunting vs. Traditional Security
| Aspect | Traditional Security | Threat Hunting |
|---|---|---|
| Approach | Reactive | Proactive |
| Trigger | Alerts and incidents | Hypotheses and intelligence |
| Timeline | Post-incident | Pre-incident |
| Data Usage | Alert correlation | Deep data analysis |
| Skills Required | Incident response | Analytical and investigative |
🎯 Threat Hunting Methodologies
1. Hypothesis-Driven Hunting
Definition: Hunting based on educated assumptions about attacker behavior and techniques.
📋 Process:
- Hypothesis Formation: Develop assumptions about potential threats
- Data Collection: Gather relevant data sources
- Analysis: Search for indicators matching the hypothesis
- Validation: Confirm or refute the hypothesis
- Documentation: Record findings and lessons learned
🔍 Example Hypotheses:
- "Advanced persistent threats may use PowerShell for persistence"
- "Insider threats might access sensitive data during off-hours"
- "Malware may communicate with C2 servers using DNS tunneling"
✅ Advantages:
- Focused and targeted approach
- Based on threat intelligence
- Efficient use of resources
❌ Limitations:
- May miss unknown techniques
- Requires strong threat intelligence
- Limited by analyst expertise
2. Data-Driven Hunting
Definition: Hunting based on anomalies and patterns discovered in data analysis.
📋 Process:
- Data Collection: Gather comprehensive data from multiple sources
- Baseline Establishment: Understand normal behavior patterns
- Anomaly Detection: Identify deviations from normal patterns
- Investigation: Analyze anomalies for malicious activity
- Validation: Confirm whether anomalies represent threats
🔍 Example Techniques:
- Statistical analysis of network traffic
- Machine learning-based anomaly detection
- Behavioral analysis of user activities
- Time-series analysis of system events
✅ Advantages:
- Can detect unknown threats
- Objective and data-driven
- Scalable with automation
❌ Limitations:
- High false positive rates
- Requires extensive data
- Complex baseline establishment
3. Threat Intelligence-Driven Hunting
Definition: Hunting based on specific threat intelligence about actors, campaigns, or techniques.
📋 Process:
- Intelligence Collection: Gather threat intelligence from various sources
- Relevance Assessment: Determine applicability to your environment
- Indicator Development: Create hunting indicators from intelligence
- Search Execution: Hunt for specific indicators in your data
- Campaign Analysis: Investigate broader attack campaigns
🔍 Example Intelligence Sources:
- MITRE ATT&CK framework
- Threat intelligence feeds
- Industry reports and advisories
- Government threat bulletins
- Security vendor intelligence
✅ Advantages:
- Highly targeted and relevant
- Based on real-world threats
- Continuously updated
❌ Limitations:
- Dependent on intelligence quality
- May focus on known threats only
- Requires intelligence processing capabilities
4. Asset-Driven Hunting
Definition: Hunting focused on protecting specific high-value assets or systems.
📋 Process:
- Asset Identification: Identify critical assets and systems
- Attack Surface Analysis: Understand potential attack vectors
- Threat Modeling: Identify likely attack scenarios
- Monitoring Implementation: Deploy focused monitoring
- Continuous Hunting: Regularly hunt for threats to these assets
🔍 Example Assets:
- Domain controllers
- Database servers
- Executive workstations
- Critical infrastructure systems
- Financial systems
✅ Advantages:
- Focuses on critical assets
- Risk-based approach
- Resource-efficient
❌ Limitations:
- May miss threats to other assets
- Requires accurate asset inventory
- Limited scope
📊 Hunting Maturity Models
SANS Threat Hunting Maturity Model
The SANS Institute has developed a maturity model to assess and improve threat hunting capabilities across organizations.
Level 0: Initial
Characteristics: No formal threat hunting program
- Reactive security approach
- Incident-driven investigations
- Limited hunting capabilities
- No dedicated hunting team
Level 1: Minimal
Characteristics: Basic hunting capabilities with some formalization
- Ad-hoc hunting activities
- Basic tools and processes
- Limited data sources
- Some documentation
Level 2: Procedural
Characteristics: Established hunting procedures and processes
- Formal hunting procedures
- Regular hunting activities
- Multiple data sources
- Basic automation
Level 3: Innovative
Characteristics: Advanced hunting with innovation and adaptation
- Advanced hunting techniques
- Custom tools and automation
- Threat intelligence integration
- Continuous improvement
Level 4: Leading
Characteristics: Industry-leading hunting capabilities
- Cutting-edge hunting methods
- Advanced automation and AI
- Threat intelligence creation
- Industry leadership
🧪 Hands-On Exercise
Exercise: Methodology Selection and Planning
Objective: Practice selecting appropriate hunting methodologies for different scenarios.
📋 Scenarios:
Scenario 1: Ransomware Campaign
Situation: Your organization has received intelligence about a new ransomware campaign targeting your industry.
Requirements:
- Determine the most appropriate hunting methodology
- Develop a hunting plan
- Identify required data sources
- Create hunting hypotheses
Scenario 2: Insider Threat Investigation
Situation: Suspicious activities detected on a high-privilege user account during off-hours.
Requirements:
- Select appropriate hunting approach
- Define investigation scope
- Identify key data points
- Develop analysis strategy
Scenario 3: Zero-Day Vulnerability
Situation: A zero-day vulnerability has been disclosed affecting software used in your environment.
Requirements:
- Choose hunting methodology
- Plan immediate response
- Identify monitoring requirements
- Develop detection strategies
📄 Deliverables:
- Methodology selection rationale for each scenario
- Detailed hunting plan for one scenario
- Data source requirements matrix
- Success criteria and metrics