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mukul975
886658219f
- Add mitre_f3 frontmatter block to 94 fraud-relevant skills (phishing, account takeover, banking malware, BEC, identity/KYC, payment/card fraud, money-mule/cash-out, ransomware extortion, DFIR, threat intel) - Map each skill to F3 v1.1 tactics + precise technique IDs, including the two F3-specific tactics ATT&CK lacks: Positioning (FA0001) and Monetization (FA0002) - All 123 F3 v1.1 technique IDs validated against the upstream STIX bundle (github.com/center-for-threat-informed-defense/fight-fraud-framework): 0 invalid IDs, 0 invalid tactics, 0 name mismatches, no placeholder IDs - mitre_f3 kept as a separate block from mitre_attack (F3 redefines several ATT&CK tactics for the fraud context) - Add docs/mitre-f3-mapping.md schema reference - Update README: F3 as the 6th framework, dedicated F3 section + badge
106 lines
2.9 KiB
Markdown
106 lines
2.9 KiB
Markdown
---
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name: hunting-credential-stuffing-attacks
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description: 'Detects credential stuffing attacks by analyzing authentication logs
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for login velocity anomalies, ASN diversity, password spray patterns, and geographic
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distribution of failed logins. Uses statistical analysis on Splunk or raw log data.
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Use when investigating account takeover campaigns or building detection rules for
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auth abuse.
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'
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domain: cybersecurity
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subdomain: security-operations
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tags:
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- credential-stuffing
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- authentication-logs
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- login-anomaly
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- asn-analysis
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- threat-hunting
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- account-takeover
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- DE.CM-01
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- RS.MA-01
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- GV.OV-01
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- DE.AE-02
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mitre_attack:
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- T1078
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- T1190
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- T1059
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- T1003
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- T1110
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mitre_f3:
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version: '1.1'
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tactics:
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- initial-access
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- positioning
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techniques:
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- id: T1110.004
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name: 'Brute Force: Credential Stuffing'
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tactic: initial-access
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source: attack
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- id: T1110.003
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name: 'Brute Force: Password Spraying'
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tactic: initial-access
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source: attack
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- id: F1006.002
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name: 'Account Takeover: Exposed Login Credential'
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tactic: initial-access
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source: f3
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- id: F1006
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name: Account Takeover
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tactic: initial-access
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source: f3
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---
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# Hunting Credential Stuffing Attacks
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## When to Use
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- When investigating security incidents that require hunting credential stuffing attacks
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Familiarity with security operations concepts and tools
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- Access to a test or lab environment for safe execution
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- Python 3.8+ with required dependencies installed
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- Appropriate authorization for any testing activities
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## Instructions
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Analyze authentication logs to detect credential stuffing by identifying patterns
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of distributed login failures, high IP diversity, and suspicious ASN distribution.
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```python
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import pandas as pd
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from collections import Counter
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# Load auth logs
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df = pd.read_csv("auth_logs.csv", parse_dates=["timestamp"])
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# Credential stuffing indicator: many IPs trying few accounts
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ip_per_account = df[df["status"] == "failed"].groupby("username")["source_ip"].nunique()
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accounts_under_attack = ip_per_account[ip_per_account > 50]
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```
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Key detection indicators:
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1. High unique source IPs per failed username
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2. Low success rate across many accounts (< 1%)
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3. ASN concentration from cloud/proxy providers
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4. Geographic impossibility (same account, distant locations)
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5. User-agent uniformity across distributed IPs
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## Examples
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```python
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# Password spray: one password tried across many accounts
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spray = df[df["status"] == "failed"].groupby(["source_ip", "password_hash"]).agg(
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accounts=("username", "nunique")).reset_index()
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sprays = spray[spray["accounts"] > 10]
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```
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