detecting-rdp-brute-force-attacks
Detect RDP brute force attacks by analyzing Windows Security Event Logs for failed authentication patterns (Event ID 4625), successful logons after failures (Event ID 4624), NLA failures, and source IP frequency analysis.
Best use case
detecting-rdp-brute-force-attacks is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Detect RDP brute force attacks by analyzing Windows Security Event Logs for failed authentication patterns (Event ID 4625), successful logons after failures (Event ID 4624), NLA failures, and source IP frequency analysis.
Teams using detecting-rdp-brute-force-attacks should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/detecting-rdp-brute-force-attacks/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How detecting-rdp-brute-force-attacks Compares
| Feature / Agent | detecting-rdp-brute-force-attacks | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Detect RDP brute force attacks by analyzing Windows Security Event Logs for failed authentication patterns (Event ID 4625), successful logons after failures (Event ID 4624), NLA failures, and source IP frequency analysis.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
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