Advanced Threat Hunting with SIEMs, Part 1
The art of war teaches us to rely not on the likelihood of the enemy's not coming, but on our own readiness to receive him
— Sun Tzu
The Art of War
In the digital age, these words resonate deeply with cybersecurity professionals engaged in the constant battle against evolving threats. Advanced threat hunting with Security Information and Event Management (SIEM) systems embodies this proactive approach, transforming defensive postures into offensive strategies against cyber adversaries.
Threats can lurk undetected for months, traditional reactive security measures are no longer sufficient. Advanced threat hunting leverages the power of SIEM tools to proactively search for, identify, and neutralize threats before they can cause significant damage. This approach combines the vast data collection and analysis capabilities of modern SIEM systems with the expertise and intuition of skilled security analysts.
The following 20 points outline a comprehensive framework for implementing and optimizing advanced threat hunting practices using SIEM tools. From understanding your environment and collecting comprehensive data to leveraging artificial intelligence and adopting a hypothesis-driven approach, these strategies cover the entire spectrum of modern threat hunting. They emphasize the importance of not only technological solutions but also human expertise, continuous learning, and adaptive methodologies.
By embracing these practices, organizations can move beyond mere compliance and basic security monitoring. They can develop a robust, proactive security posture that not only detects and responds to known threats but also uncovers hidden, advanced persistent threats that might otherwise evade traditional security measures. In essence, these strategies transform the SIEM from a passive logging tool into a powerful weapon in the cybersecurity arsenal, enabling defenders to meet Sun Tzu's age-old advice: to be ready, vigilant, and prepared to face any digital enemy that may come.
Understand Your Environment
Understanding your environment is the foundation of effective threat hunting. It involves creating a comprehensive map of your digital landscape, including all assets, network connections, and data flows. This knowledge allows you to identify potential vulnerabilities, prioritize your security efforts, and quickly spot anomalies that could indicate a threat.
Actionable Items:
☐ Implement an automated asset discovery tool to create and maintain an up-to-date inventory
☐ Develop a network topology map that includes all known devices and connections
☐ Identify and document your organization's critical assets and "crown jewels"
☐ Configure your SIEM to use asset and identity management features for real-time inventory updates
☐ Set up collection and analysis of network flow data (e.g., Netflow, IPFIX) to understand communication patterns
☐ Establish a continuous monitoring system to detect and alert on changes in your environment
☐ Create a dashboard that visualizes your network topology, highlighting critical assets and unusual connections
☐ Implement a regular review process to identify and incorporate shadow IT and cloud resources into your asset inventory
☐ Develop and maintain documentation of your environment, including network diagrams and asset lists
☐ Establish a process for regular updates and verification of your environment understanding
By completing these actions, you'll build a solid foundation for your threat hunting efforts, enabling you to quickly identify potential threats and anomalies in your environment.
Common Mistakes
When understanding your environment, it's easy to fall into several traps. Many organizations overlook shadow IT and cloud resources, leaving significant blind spots in their asset inventory. Another frequent error is failing to keep the asset inventory and network topology maps up-to-date, which can lead to misguided threat hunting efforts. Additionally, some teams focus solely on technical assets, neglecting the human element and forgetting to map out user roles and access patterns. Lastly, overreliance on automated tools without human verification can result in incomplete or inaccurate environmental understanding. Regular manual checks and cross-verification are crucial to maintain an accurate picture of your digital landscape.
Collect Comprehensive Data
Collecting comprehensive data is a crucial step in effective threat hunting. It involves gathering logs and telemetry from all critical systems across your environment, including endpoints, network devices, servers, and cloud services. This holistic approach to data collection provides the raw material needed for in-depth analysis and threat detection, allowing you to piece together the full picture of potential security incidents.
Actionable Items
☐ Identify all critical systems and data sources in your environment
☐ Implement log forwarding from all critical systems to your SIEM
☐ Deploy universal forwarders or lightweight agents on endpoints for data collection
☐ Configure syslog collection for network devices and legacy systems
☐ Set up API-based data collection for cloud services and SaaS applications
☐ Ensure proper time synchronization across all systems using NTP or similar protocols
☐ Establish a process for regular audits of data collection coverage
☐ Implement data retention policies that balance security needs with cost considerations
☐ Set up access controls and encryption for collected data to prevent unauthorized access
☐ Create a use case to correlate endpoint logs with network traffic for detecting lateral movement
By implementing these actions, you'll ensure a comprehensive data collection strategy that provides the necessary visibility for effective threat hunting across your entire environment.
Keep in Mind
While collecting comprehensive data is essential, it's important to strike a balance between thoroughness and efficiency. Collecting too much data can lead to storage issues, increased costs, and analysis paralysis. Focus on collecting high-quality, relevant data rather than trying to capture everything. Also, be aware of any legal or compliance issues related to data collection, especially when dealing with personal information or operating in multiple jurisdictions. Regularly review and optimize your data collection strategy to ensure it aligns with your threat hunting objectives and doesn't become a burden on your systems or analysis capabilities.
Data Normalization
Data normalization is a critical process in threat hunting that involves transforming diverse log formats into a standardized structure. This uniformity allows for easier correlation, analysis, and searching across different data sources. By implementing a common event schema and enriching data with additional context, you create a more cohesive and insightful dataset for threat detection and investigation.
Actionable Items
☐ Choose and implement a common event schema (e.g., Elastic Common Schema, Splunk Common Information Model)
☐ Identify key fields across different log sources that need to be normalized
☐ Configure field extraction and parsing rules in your SIEM to standardize log formats
☐ Set up data enrichment processes to add context like geolocation and threat intelligence
☐ Utilize field extraction and transformation features native to your SIEM
☐ Implement data pipelines for more complex transformations that your SIEM can't handle natively
☐ Create a normalization map that documents how fields from various sources map to your common schema
☐ Establish a process for regularly reviewing and updating normalization rules as log formats change
☐ Develop a use case to normalize authentication events from different sources for a unified view of login activities
☐ Implement quality checks to ensure normalized data accurately represents the original log information
By following these steps, you'll create a normalized dataset that enables more effective threat hunting and analysis across your entire environment.
Gotchas
When normalizing data, be cautious of several common pitfalls. Inconsistent time formats across different systems can lead to incorrect event sequencing, potentially obscuring the true timeline of an incident. Over-normalization is another risk, where valuable unique data might be lost in an attempt to fit everything into a standardized format. Be mindful of maintaining a balance between normalization and preserving important source-specific details. Additionally, be wary of normalization errors that could introduce false data or relationships. Regular audits of your normalized data against raw logs are crucial to catch and correct any discrepancies. Lastly, remember that normalization is an ongoing process – as your environment evolves and new data sources are added, your normalization rules will need to be updated accordingly.
Threat Intelligence Integration
Threat intelligence integration is a pivotal component of advanced threat hunting, providing crucial context and indicators to guide your investigations. By incorporating diverse threat feeds and correlating them with your internal telemetry, you can significantly enhance your ability to detect and respond to emerging threats. This process involves not just consuming threat data, but actively applying it to your specific environment and use cases.
Actionable Items
☐ Identify and subscribe to relevant commercial, open-source, and industry-specific threat feeds
☐ Implement an automated system for regular updates of threat intelligence data
☐ Configure your SIEM to use built-in threat intelligence frameworks or plugins
☐ Set up STIX/TAXII feeds for standardized threat intelligence sharing
☐ Develop processes to correlate internal telemetry with external threat data
☐ Create custom alerting rules based on high-priority threat indicators
☐ Establish a workflow for retroactive hunting using new threat intelligence
☐ Implement a use case to detect communication with known malicious IPs or domains
☐ Develop a system for scoring and prioritizing threat intelligence based on relevance to your environment
☐ Establish a regular review process to assess the effectiveness of your threat intelligence integration
By implementing these actions, you'll create a robust threat intelligence framework that enhances your threat hunting capabilities and improves your overall security posture
Tripping Hazards
While integrating threat intelligence can greatly enhance your threat hunting capabilities, there are several potential pitfalls to be aware of. One major tripping hazard is the high false positive rate that can occur if threat feeds are not properly curated or contextualized for your environment. This can lead to alert fatigue and wasted resources investigating benign activities. Another common issue is the improper handling of the Indicators of Compromise (IoCs) lifecycle. Failing to properly add, age, and retire IoCs can result in outdated alerts and missed current threats. Additionally, over-reliance on automated threat intelligence without human analysis can lead to blind spots, as sophisticated attackers may intentionally avoid known indicators. Lastly, neglecting to tailor threat intelligence to your specific industry, geography, or technology stack can result in a flood of irrelevant data that obscures truly important threats. Regular review and refinement of your threat intelligence strategy is crucial to avoid these potential pitfalls.
Behavioral Analysis
Behavioral analysis is a sophisticated approach to threat hunting that focuses on identifying anomalies in user, system, and network activities. By establishing baselines of normal behavior and leveraging advanced analytics techniques, you can detect subtle deviations that may indicate potential security threats. This method is particularly effective in uncovering insider threats, compromised accounts, and advanced persistent threats that might evade traditional signature-based detection.
Actionable Items
☐ Establish baselines for normal behavior across users, systems, and network activities
☐ Implement statistical analysis tools to identify deviations from these baselines
☐ Set up machine learning models for more advanced pattern recognition
☐ Configure peer group analysis to detect anomalies within similar user or system groups
☐ Leverage built-in machine learning toolkits or features in your SIEM
☐ Implement User and Entity Behavior Analytics (UEBA) for comprehensive behavioral monitoring
☐ Apply clustering techniques to group similar behaviors and identify outliers
☐ Set up time series analysis to detect unusual patterns over time
☐ Create a use case to detect unusual login patterns that might indicate compromised credentials
☐ Establish a process for regular review and refinement of your behavioral analysis models
By implementing these actions, you'll enhance your ability to detect subtle, behavior-based indicators of compromise that might otherwise go unnoticed.
Keep in Mind
While behavioral analysis can be a powerful tool in your threat hunting arsenal, it's important to be aware of its challenges. This approach can be computationally expensive, potentially impacting the performance of your SIEM if not properly managed. It's crucial to balance the depth of analysis with system resources. Additionally, behavioral analysis can be prone to false positives, especially when there are legitimate changes in user or system behavior (such as during role changes, system updates, or business process modifications). Regular tuning and context-aware alerting are necessary to maintain accuracy. It's also important to remember that establishing accurate baselines takes time and requires a good understanding of your environment's normal operations. Lastly, while behavioral analysis can detect subtle anomalies, it should be used in conjunction with other threat hunting techniques for a comprehensive security approach. Always correlate behavioral anomalies with other indicators of compromise for more accurate threat detection.
