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50 lines
1.6 KiB
Markdown
50 lines
1.6 KiB
Markdown
---
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name: hunting-credential-stuffing-attacks
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description: >
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Detects credential stuffing attacks by analyzing authentication logs for login velocity
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anomalies, ASN diversity, password spray patterns, and geographic distribution of failed
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logins. Uses statistical analysis on Splunk or raw log data. Use when investigating
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account takeover campaigns or building detection rules for auth abuse.
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domain: cybersecurity
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subdomain: security-operations
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tags: [hunting, credential, stuffing, attacks]
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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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---
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# Hunting Credential Stuffing Attacks
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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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