Advanced Threat Hunting with EDR: Building Custom Detection Rules

 

In today's complex threat landscape, relying solely on automated security solutions isn't enough. Advanced threat actors are constantly evolving their tactics, techniques, and procedures (TTPs) to evade traditional security measures. This is where advanced threat hunting comes in, empowered by Endpoint Detection and Response (EDR) solutions and fueled by custom detection rules.

Understanding the Core Components

Before diving into building custom detection rules, let's establish a firm understanding of the key technologies and frameworks involved:

EDR (Endpoint Detection and Response)

EDR solutions provide real-time visibility into endpoint activity, enabling security teams to detect, investigate, and respond to threats. Key capabilities include:

• Endpoint telemetry: Collecting comprehensive data from endpoints, including process executions, file modifications, network connections, and registry changes.
• Behavioral analysis: Identifying suspicious activities based on deviations from normal behavior patterns.
• Automated response: Allowing for immediate actions such as isolating infected endpoints, killing malicious processes, and removing malicious files.
• Threat intelligence integration: Correlating endpoint data with threat intelligence feeds to identify known threats and indicators of compromise (IOCs).

Threat Hunting

Threat hunting is a proactive security activity focused on searching for malicious activity that has evaded automated security controls. It involves:

• Developing hypotheses: Formulating educated guesses about potential attacker activities based on threat intelligence, security alerts, and internal observations.
• Searching for evidence: Using EDR data and other security information to validate or disprove hypotheses.
• Analyzing findings: Understanding the scope and impact of discovered threats.
• Taking remediation actions: Containing and eradicating threats.
• Improving defenses: Learning from past incidents to enhance security controls and prevent future attacks.

SIEM (Security Information and Event Management)

SIEM systems aggregate and analyze security data from various sources across the organization, providing a centralized view of security events. While EDR focuses on endpoints, SIEM provides broader visibility across the network, servers, and cloud environments.

MITRE ATT&CK Framework

The MITRE ATT&CK framework is a globally accessible knowledge base of adversary tactics and techniques based on real-world observations. It provides a common language for describing attacker behavior and helps security teams understand and defend against specific threats. It is organized in a matrix structure with tactics as columns and techniques as rows.

Sigma Rules

Sigma is a generic and open signature format that allows you to describe relevant log events in a straightforward manner. The rule format is very flexible, easy to write, and applicable to many different log sources. Sigma rules can be converted into specific query languages for various SIEM and EDR platforms, making them a valuable tool for sharing and implementing detection logic.

Building Custom Detection Rules: A Step-by-Step Guide

Creating effective custom detection rules is crucial for proactively identifying advanced threats. Here's a detailed guide:

1. Define the Objective: What specific threat or attacker behavior are you trying to detect? For example, are you trying to detect lateral movement using specific tools? Identify the specific MITRE ATT&CK technique(s) you want to address.
2. Identify Relevant Data Sources: Determine which log sources and EDR telemetry contain the information needed to detect the targeted behavior. Common data sources include process execution logs, network connection logs, file modification logs, and registry changes.
3. Craft the Rule Logic: Develop the logic for the detection rule based on the identified data sources and the targeted behavior. Use specific keywords, patterns, and thresholds to identify suspicious activity. This is where tools like Sigma shine.
4. Test and Tune the Rule: Test the rule against historical data and live traffic to ensure it accurately identifies the targeted behavior without generating excessive false positives. Fine-tune the rule based on the results of testing. Iterative testing is key.
5. Implement the Rule: Deploy the rule in your EDR or SIEM platform. Ensure that the rule is properly configured and integrated with other security tools and processes.
6. Monitor and Maintain the Rule: Continuously monitor the performance of the rule and make adjustments as needed to maintain its effectiveness. Regularly review and update the rule based on changes in the threat landscape and attacker TTPs.

Example: Detecting Suspicious PowerShell Activity Using Sigma

Let's consider an example of detecting suspicious PowerShell activity using a Sigma rule. Many attackers use PowerShell to download and execute malicious code.

Objective: Detect PowerShell executions that download files from external sources.

Sigma Rule (YAML):

title: Suspicious PowerShell Download Activity
id: example-powershell-download
status: experimental
description: Detects PowerShell scripts that download files from the internet.
author: ByteSectorX
date: 2024/10/27
logsource:
  category: process_creation
  product: windows
detection:
  selection:
    process.executable:
      - '*\powershell.exe'
    process.command_line|contains:
      - 'IEX (New-Object Net.WebClient).DownloadString('
      - 'Invoke-WebRequest'
  condition: selection
level: high

Explanation:

• title: A descriptive name for the rule.
• id: A unique identifier for the rule.
• logsource: Specifies the data source (process creation logs from Windows).
• detection: Defines the conditions for triggering the rule. It looks for processes named "powershell.exe" with command lines containing phrases commonly used for downloading files via PowerShell.
• condition: Specifies that the `selection` condition must be met for the rule to trigger.
• level: Indicates the severity of the alert (high in this case).

This Sigma rule can be converted into the specific query language required by your EDR or SIEM platform (e.g., Splunk, Elastic, CrowdStrike). The converted query will then search for events that match the defined criteria.

Best Practices for Building Effective Detection Rules

• Start with Known Threats: Focus on developing rules to detect known threats and vulnerabilities.
• Prioritize High-Fidelity Detections: Aim for rules that generate a low number of false positives.
• Leverage Threat Intelligence: Integrate threat intelligence feeds to identify known IOCs and attacker TTPs.
• Use a Modular Approach: Break down complex threats into smaller, more manageable detection rules.
• Document Your Rules: Clearly document the purpose, logic, and testing results for each rule.
• Automate Where Possible: Automate the process of creating, testing, and deploying detection rules. Consider using tools that can automatically translate Sigma rules into platform-specific queries.
• Collaborate and Share: Share your detection rules with the security community to improve overall threat detection capabilities.

Conclusion

Advanced threat hunting with EDR and custom detection rules is essential for staying ahead of sophisticated attackers. By understanding the core components, following a structured approach to building rules, and adhering to best practices, security teams can proactively identify and respond to threats before they cause significant damage. Investing in EDR solutions, developing threat hunting capabilities, and building custom detection rules are critical investments for any organization seeking to strengthen its cybersecurity posture. Embrace the power of tools like Sigma and the knowledge within the MITRE ATT&CK framework to elevate your threat hunting game and protect your organization from evolving cyber threats.

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