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Introduction:
In the ever-escalating arms race of cybersecurity, your SIEM is only as effective as the rules powering it. A new community-driven pack for Microsoft Sentinel shifts focus to a frequently overlooked data goldmine: the `CommonSecurityLog` (CEF) table. This repository provides security teams with a formidable arsenal of 31 KQL threat-hunting queries and 37 analytic rules designed to detect sophisticated threats across Fortinet, Palo Alto Networks, and Zscaler infrastructure—catching everything from beaconing firewalls to “low & slow” data exfiltration that traditional perimeter defenses often miss.
Learning Objectives:
- Objective 1: Understand how to deploy community-sourced ARM templates to instantly enhance Microsoft Sentinel’s detection capabilities.
- Objective 2: Master the art of hunting for firewall beaconing, anomalous outbound traffic, and Zscaler usage anomalies (impossible travel, multi-channel exfiltration) using KQL.
- Objective 3: Learn to extend Sentinel’s native capabilities by focusing on the underutilized CommonSecurityLog table for multi-vendor threat detection.
You Should Know:
1. Repository Overview and Initial Setup
This isn’t just a list of queries; it’s a plug-and-play detection framework. The GitHub repository shared by David Alonso Dominguez focuses exclusively on the `CommonSecurityLog` table, which aggregates logs from legacy devices and firewalls via the Common Event Format (CEF). The pack is tailored for three major vendors: Fortinet (FortiGate), Palo Alto Networks, and Zscaler (ZIA/ZPA).
– Step‑by‑step guide:
1. Navigate to the repository: `https://github.com/davidalonsod/Dalonso-Security-Repo/tree/main/Use%20Cases%20Threat%20Hunting/CommonSecurityLog-ThreatHunting`
2. Clone the repository locally or review the raw KQL files and the ARM template.
3. Deployment: In Microsoft Sentinel, go to “Content management” > “Content hub” > “Add via ARM template”. Upload the provided `azuredeploy.json` file. This will deploy all 37 analytic rules simultaneously, linking them to the correct data sources.
2. Hunting Firewall Beaconing and Anomalous Outbound Traffic
Traditional EDR solutions might miss beaconing if it originates from a network device rather than an endpoint. This pack contains specific KQL queries for Fortinet and Palo Alto logs to identify potential Command & Control (C2) communication.
– Step‑by‑step guide:
– The Query Concept: The queries look for consistent, periodic outbound connections from a single internal IP to an external domain/IP over time.
– Linux Verification (Beaconing Simulation): To test detection, you can simulate beaconing from a Linux client behind the firewall using a simple cron job or a bash loop:
!/bin/bash Simulate beaconing to a test server every 60 seconds while true; do curl -s https://your-test-c2-server.com/ping > /dev/null sleep 60 done
– Windows Verification (PowerShell): Similarly, on a Windows host, you can use PowerShell:
while ($true) {
Invoke-WebRequest -Uri https://your-test-c2-server.com/ping -UseBasicParsing | Out-Null
Start-Sleep -Seconds 60
}
– The KQL query in Sentinel will aggregate these connections, flagging the regularity as suspicious.
- Zscaler Anomaly Detection: Impossible Travel and Multi-Channel Exfiltration
Thanks to contributions from Zscaler experts, the pack includes advanced logic to detect misuse of Secure Web Gateways (SWG) and Zero Trust Network Access (ZTNA). The focus is on two primary threats: “Impossible Travel” (logins from geographically distant locations within an impossible timeframe) and “Low & Slow” exfiltration via multiple channels (HTTP, FTP, SSH) from a single user.
– Step‑by‑step guide:
– Detection Logic: The queries cross-reference Zscaler logs (CommonSecurityLog) where `DeviceVendor` == ‘Zscaler’. It calculates the time difference between log events from the same user but different source geolocations (if geolocation is enabled).
– Mitigation Strategy: If triggered, a Security Orchestration Automation and Response (SOAR) playbook can be triggered to block the user’s IP at the Zscaler policy level via API calls.
4. KQL Deep Dive: Extending the Queries
While the ARM template is useful, understanding the underlying KQL allows for customization.
– Step‑by‑step guide:
– Open Microsoft Sentinel > Logs.
– Target the `CommonSecurityLog` table.
– Example snippet to view Fortinet traffic flagged by the new rules:
CommonSecurityLog | where TimeGenerated > ago(24h) | where DeviceVendor == "Fortinet" | where DeviceProduct == "FortiGate" | where isnotempty(DestinationIP) | summarize ConnectionCount = count() by SourceIP, DestinationIP, DestinationPort | where ConnectionCount > 100 //Threshold for beaconing
– Modify thresholds based on your environment’s baseline to reduce false positives.
5. Testing and Validation with Atomic Red Team
To ensure these rules fire correctly without waiting for a real breach, security teams should validate them.
– Step‑by‑step guide:
– Use the Atomic Red Team framework to simulate techniques like T1048 (Exfiltration Over Alternative Protocol).
– Run a test that forces traffic through a proxy (simulating Zscaler) to generate logs in the `CommonSecurityLog` table.
– Verify that the newly deployed analytic rule creates an incident in Sentinel.
What Undercode Say:
- Key Takeaway 1: The release of this community pack democratizes advanced threat hunting, specifically targeting network-layer telemetry that is often ignored by cloud-native SIEM defaults.
- Key Takeaway 2: The collaboration between Microsoft engineers and Zscaler specialists highlights the industry shift towards multi-vendor detection logic, breaking down silos between network security and endpoint detection.
Analysis:
This initiative is a significant force multiplier for Security Operations Centers (SOCs) operating on a budget. By utilizing the `CommonSecurityLog` table, it addresses a critical blind spot: the network perimeter. Many organizations ingest CEF logs for compliance but lack the parsing logic to use them for proactive hunting. This pack essentially retrofits legacy firewalls and modern gateways with next-gen detection capabilities. The inclusion of an ARM template lowers the barrier to entry, allowing even understaffed teams to deploy enterprise-grade analytics in minutes. It serves as a reminder that in cybersecurity, the data you already have is often your most potent weapon—you just need the right queries to unlock it.
Prediction:
We will see a surge in “Community Detection Engineering,” where threat hunters share granular, vendor-specific KQL (or Splunk SPL) as open-source packages. This will force SIEM vendors to improve their native analytics to match the pace and specificity of community innovation. Furthermore, expect Microsoft to eventually absorb these high-fidelity detections into its official Threat Analytics solutions, validating the community’s work as the new baseline for network-layer security in the cloud.
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IT/Security Reporter URL:
Reported By: David Alonso – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
