Full Report
How It Works The showcased feature translates a Linux-based Sigma rule — specifically targeting the sysinfo system call — into Microsoft Sentinel KQL. This system call provides an attacker with system metadata like uptime, memory usage, and load averages — commonly abused during reconnaissance. Left Panel – Sigma Rule: Targets Linux auditd telemetry for syscall […] The post Detect Linux Reconnaissance in Microsoft Sentinel with Sigma-to-KQL Conversion appeared first on SOC Prime.
Analysis Summary
This article focuses on a procedural improvement for threat detection engineering rather than detailing specific malware or threat actors. The primary subject is the conversion of threat detection rules written in Sigma format specifically for detecting **Linux Reconnaissance** into Kusto Query Language (KQL) for Microsoft Sentinel.
# Tool/Technique: Sigma-to-KQL Conversion for Linux Reconnaissance Detection
## Overview
This focuses on the automated conversion process that allows security teams to operationalize Linux-based threat detection rules (written in the generic Sigma format) directly into KQL queries compatible with Microsoft Sentinel. The goal is to enhance threat coverage across hybrid cloud and Linux infrastructure by streamlining detection engineering without manual KQL scripting.
## Technical Details
- Type: Attack Tool/Procedure (Detection Engineering Tool/Process)
- Platform: Linux infrastructure logs ingested into Microsoft Sentinel (KQL environment)
- Capabilities: Automated conversion of Sigma detection logic to KQL, preserving custom filters and fidelity, speeding up deployment of Linux threat detections.
- First Seen: Not specified (focus is on the present capability via Uncoder AI/SOC Prime platform features)
## MITRE ATT&CK Mapping
The underlying detection targets reconnaissance activities typically associated with the **Reconnaissance** tactic.
- **TA0043 - Reconnaissance**
- **T1595 - Active Scanning** (Potential mapping, depending on Sigma rule details, e.g., scanning specific system files/logs)
- **T1082 - System Information Discovery** (Likely mapping, as accessing system logs/files is a form of discovery)
*Note: Since the specific Sigma rule content is not provided, the MITRE mapping is generalized based on the detection goal ("Linux Reconnaissance").*
## Functionality
### Core Capabilities
- **Cross-Platform Detection Normalization:** Converts detection logic across different environments.
- **Reconnaissance Detection:** Provides tactical detection capabilities specifically aimed at early-stage attacks involving information gathering on Linux systems (e.g., reading specific log files or executing common discovery commands).
- **Logic Preservation:** Ensures the fidelity and custom filters defined in the original Sigma rule are accurately maintained during the KQL translation.
### Advanced Features
- **No Manual KQL Scripting:** Eliminates the need for security team members to manually write or debug KQL for new Linux detections.
- **Integration with Sentinel:** Operationalizes detections directly into cloud-native SIEMs like Microsoft Sentinel.
## Indicators of Compromise
This summary does not detail specific IoCs related to malware, as it describes a detection mechanism. The relevant "indicators" are related to the **source format** and **target environment**:
- File Hashes: N/A
- File Names: N/A
- Registry Keys: N/A
- Network Indicators: N/A
- Behavioral Indicators: System activities indicative of Linux reconnaissance (the content of the Sigma rule itself, which is not laid out).
## Associated Threat Actors
Specific threat actors are not mentioned in the provided context. However, the capability is designed to detect intrusions that utilize Linux reconnaissance techniques employed by various threat groups targeting Linux servers (e.g., APTs like APT28, etc., depending on the detection deployed).
## Detection Methods
The core methodology discussed is the effective translation and deployment of Sigma rules into KQL for detection in Sentinel.
- Signature-based detection: Enabled via the deployed KQL query derived from the Sigma rule.
- Behavioral detection: Implied, as reconnaissance often involves specific command executions or log access patterns that Sigma rules target behaviorally.
- YARA rules if available: N/A
## Mitigation Strategies
Mitigation focuses on improving the *detection engineering process* rather than patching a specific vulnerability or malware.
- **Prevention measures:** Deploying high-fidelity, converted Linux reconnaissance rules into Microsoft Sentinel promptly.
- **Hardening recommendations:** Utilizing automated tools (like Uncoder AI) to reduce detection engineering bottlenecks and ensuring broad coverage across hybrid environments.
## Related Tools/Techniques
- **Sigma:** The open-source detection language used as the source input.
- **Kusto Query Language (KQL):** The target query language for Microsoft Sentinel.
- **Microsoft Sentinel:** The target SIEM platform.
- **Uncoder AI:** The presumed tool facilitating the Sigma-to-KQL conversion process.