Empowering Detection Engineering with Chronicle and Mandiant Security Validation

[dnehoda]

Empowering Detection Engineering with Chronicle and Mandiant Security Validation

Introduction

Detection engineers face the relentless task of creating, refining, and validating SIEM rules to keep pace with evolving threats.  Ad hoc testing and reliance on simulated attacks often lead to blind spots and false positives. Mandiant Security Validation (MSV) offers a systematic, attack-centric approach to streamline rule development and proactively uncover weaknesses in your detection logic.

MSV for the Detection Engineer

1. Beyond Vulnerability Scans: MSV tests your entire detection pipeline, not just isolated attack components. This uncovers gaps where SIEM rules may be too narrow or fail to correlate events across the attack lifecycle.
2. Actionable Data: MSV doesn't just tell you if a rule works. It pinpoints exactly why an attack was missed (lack of required logs, flawed rule logic) or identifies the stages where existing rules trigger, highlighting opportunities for refinement.
3. Threat-Driven Prioritization: Integrate MSV with your threat intelligence feeds. Focus testing on the latest TTPs targeting your industry, ensuring your detection rules align with the threats you are most likely to face.

MSV in Your Workflow

1. New Rule Development:
• Test custom rules in MSV before deployment. Create attack scenarios that exercise their logic and ensure they trigger as intended.
• Identify potential false positives early, streamlining the deployment process.
2. Iterative Refinement:
• Use MSV's detailed reports to pinpoint why existing rules miss attacks.
• Modify rules, retest in MSV, and track improvement in detection accuracy over time.
3. Proving Effectiveness:
• Quantify the value of your detection engineering efforts with metrics. Demonstrate improvement in rule coverage, reduction in false positives, and reduced dwell time (due to faster detection).

Operationalizing MSV for Detection Teams

• Collaboration Hub: MSV's reports become a shared language between detection engineers and SOC analysts. Use findings to bridge the gap between rule creation and real-world incident response.
• Knowledge Base: Annotate MSV attack scenarios with relevant MITRE ATT&CK references, SIEM rule correlations, and troubleshooting notes. Create a centralized resource for your team.
• Change Management Integration: Incorporate MSV testing results into your change control processes. Provide evidence to justify rule updates and validate their potential impact before production rollout.

Advanced Use Cases

• Testing New Log Sources: Before writing new detections, use MSV to generate relevant events. Confirm that your SIEM parses the logs correctly and that rules fire as expected.
• Red Team Emulation: Coordinate with internal Red Teams to use MSV attacks during exercises. Validate blue team detection capabilities in a realistic scenario.

Call to Action

Elevate your detection engineering with the power of real-world attack testing. Mandiant Security Validation provides the insights and evidence necessary to build and maintain a robust SIEM detection program.

Scenario: Detecting Kerberoasting Attempts

Kerberoasting is a common post-exploitation technique where adversaries extract service account credentials from Active Directory for offline cracking, enabling privilege escalation and lateral movement.

Chronicle SIEM Rule (YARA-L)

rule detect_kerberoast_request 

 {

    meta:

        author = "Your Team Name"

        description = "Detects Kerberos TGS-REQ messages requesting encryption type RC4_HMAC_NT (weak cipher)"

        reference = "https://attack.mitre.org/techniques/T1558/003" 

events:

    $e.metadata.log_type = "WINEVTLOG"

    $e.metadata.product_event_type = "4769" 

    $e.metadata.description = /0x17/  //Downgrade to RC4 Encryption

    and $e.metadata.description = /0x40810000/

outcome:

    $risk_score = 75

condition:

       $e

}

Rule Purpose:

This rule is designed to detect Kerberoasting attacks within Windows Event Logs. Here's how it works:

Meta:

• author: Identifies the rule's creator or responsible team.
• description: Provides a brief explanation of the rule's intent.
• reference: Links to the MITRE ATT&CK technique (T1558.003 - Kerberoasting) for context.

Events:

• $e.metadata.log_type = "WINEVTLOG": Specifies that the rule triggers on Windows Event Logs.
• $e.metadata.product_event_type = "4769" : Filters for event IDs 4769, which are "A Kerberos service ticket was requested" events. These are essential for detecting Kerberoasting.
• $e.metadata.description = /0x17/ and $e.metadata.description = /0x40810000/": These lines look for specific hexadecimal values within the event's description field, which indicate:
• 0x17: Encryption type RC4_HMAC_NT (the targeted weak cipher).
• 0x40810000: Options flag typically associated with Kerberoasting requests.

Outcome:

• $risk_score = 75: Assigns a risk score of 75 to any event matching the rule's condition, suggesting a high likelihood of a Kerberoasting attempt.

Condition:

• $e: Specifies that all the previously defined event filters must be met for the rule to trigger.

Key Points:

• Windows-Centric: This rule is specifically for Windows environments relying on their event logs.
• Focused Detection: It looks for concrete indicators within Kerberos service ticket request events, aiming for precise Kerberoasting detection.

Areas for Potential Improvement:

• False Positives: Some legitimate applications might use RC4_HMAC_NT. Consider adding more conditions (source IP ranges, domain controller logs, unusual account activity) to refine the rule.
• Evasion: Some Kerberoasting tools might obfuscate these values. Explore ways to make the detection more resilient. Behavior-based detections alongside this rule could increase coverage.

Testing with Mandiant Security Validation

1. Attack Selection: Choose an MSV attack chain involving Kerberoasting against a Windows system.
2. Log Forwarding: Ensure Windows Event Logs (particularly 4769 events) from the target system are sent to your SIEM.
3. Analysis:
• Check if the rule triggered. If not, did the MSV attack modify the expected event field values? Modify the rule if needed.
• If it triggered, examine for false positives. Was the attack the only cause, or do you need to refine the rule
4. Iterate and Improve:
• False Positives: Investigate ways to refine the rule conditions with additional indicators to decrease noise (specific source IPs, unusual user account behavior) if needed.
• Missed Detections: Kerberoasting might involve other encryption types. Explore MSV attacks using different techniques to expand your rule's coverage.
• Provide Context: Other stages of the Kerberoasting attack (initial recon, lateral movement) for more comprehensive detection.

Key Takeaways (For Detection Engineers)

• Behavioral Detection: MSV allows you to test beyond static indicators, verifying that your rule triggers in the context of a genuine Kerberoasting attack.
• Beyond the Trigger: MSV helps identify related malicious activity pre- or post-Kerberoasting, potentially leading to the development of more correlated detection rules.
• Demonstrating Value: Track the evolution of your Kerberoasting detection rule over time using MSV test iterations as evidence of improvement.

Important Notes

• Active Directory Logging: Ensuring you have the right AD logs feeding into your Chronicle SIEM is critical for Kerberoasting detection. MSV can also help validate if those logs are sufficient.
• Responsible Testing: Always adhere to strict change control and test safely.

Let's Go Deeper

• Can you provide examples of slight modifications attackers might use to evade string-based detection?
• How could we enhance this rule further to be even more resilient to evasion attempts?

My goal is to make this guide a practical tool for detection engineers to think critically about their YARA-L rule design and along side their validation program.