plurigrid/asi/detecting-rdp-brute-force-attacks

Detecting RDP Brute Force Attacks

Overview

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-evtx, identifying attack patterns through source IP frequency analysis, detecting NLA bypass attempts, and generating actionable detection reports.

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