Skill

detecting-rdp-brute-force-attacks

From asi

Detects RDP brute force attacks by parsing Windows Security EVTX logs with python-evtx, analyzing Event IDs 4625/4624 for failed logons, IP frequency, NLA failures, and compromises. For threat hunting and SOC analysis.

Python

security

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RDP brute force attacks target Windows Remote Desktop Protocol services by attempting rapid credential guessing against exposed RDP endpoints. Detection relies on analyzing Windows Security Event Logs for Event ID 4625 (failed logon with Logon Type 10 or 3) and correlating with Event ID 4624 (successful logon) to identify compromised accounts. This skill covers parsing EVTX files with python-ev...

SKILL.md

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asi

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Last CommitApr 4, 2026

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Detecting RDP Brute Force Attacks

Overview

When to Use

When investigating security incidents that require detecting rdp brute force attacks
When building detection rules or threat hunting queries for this domain
When SOC analysts need structured procedures for this analysis type
When validating security monitoring coverage for related attack techniques

Prerequisites

Python 3.9+ with python-evtx, lxml libraries
Windows Security EVTX log files (exported from Event Viewer or collected via WEF)
Understanding of Windows authentication Event IDs (4624, 4625, 4776)
Familiarity with RDP Logon Types (Type 3 for NLA, Type 10 for RemoteInteractive)

Steps

Step 1: Export Security Event Logs

Export Windows Security logs to EVTX format using Event Viewer or wevtutil:

wevtutil epl Security C:\logs\security.evtx

Step 2: Parse Failed Logon Events

Use python-evtx to parse Event ID 4625 entries, extracting source IP, target username, failure reason (Sub Status), and Logon Type fields.

Step 3: Analyze Attack Patterns

Identify brute force patterns by:

Counting failed logons per source IP within time windows
Detecting username spray attacks (many usernames from one IP)
Correlating 4625 failures with subsequent 4624 success from same IP

Step 4: Generate Detection Report

Produce a JSON report with top attacking IPs, targeted accounts, time-based analysis, and compromise indicators.

Expected Output

JSON report containing:

Total failed logon events and unique source IPs
Top attacking IPs ranked by failure count
Targeted usernames and failure sub-status codes
Successful logons following brute force attempts (potential compromises)
Time-series analysis of attack intensity