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mukul975
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Complete skill folder anatomy across all cybersecurity skills: - scripts/agent.py: 80-150 line Python agents using real libraries (impacket, boto3, azure-mgmt-*, kubernetes, pefile, yara, scapy, shodan, stix2, etc.) - references/api-reference.md: real API documentation with method signatures - LICENSE: MIT license for all skill folders
83 lines
2.3 KiB
Markdown
83 lines
2.3 KiB
Markdown
# Authentication Anomaly Detection API Reference
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## Azure AD Sign-In Logs (Microsoft Graph)
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```bash
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# Query sign-in logs
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GET https://graph.microsoft.com/v1.0/auditLogs/signIns?$filter=createdDateTime ge 2024-01-01
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Authorization: Bearer <token>
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# Risky sign-ins
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GET https://graph.microsoft.com/v1.0/identityProtection/riskyUsers
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```
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## Okta System Log API
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```bash
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# Query authentication events
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curl "https://your-org.okta.com/api/v1/logs?filter=eventType+eq+%22user.session.start%22&since=2024-01-01" \
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-H "Authorization: SSWS <api_token>"
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# Filter failed logins
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curl "https://your-org.okta.com/api/v1/logs?filter=outcome.result+eq+%22FAILURE%22" \
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-H "Authorization: SSWS <api_token>"
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```
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## Windows Event IDs for Auth Monitoring
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| Event ID | Description |
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|----------|-------------|
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| 4624 | Successful logon |
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| 4625 | Failed logon |
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| 4648 | Logon with explicit credentials |
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| 4672 | Special privileges assigned |
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| 4768 | Kerberos TGT request |
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| 4769 | Kerberos service ticket request |
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| 4771 | Kerberos pre-auth failed |
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| 4776 | NTLM credential validation |
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## Splunk SPL Detection Queries
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```spl
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# Brute force detection
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index=auth result="failure"
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| bin _time span=10m
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| stats count by user src_ip _time
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| where count >= 10
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# Password spray detection
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index=auth result="failure"
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| bin _time span=30m
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| stats dc(user) as targets count by src_ip _time
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| where targets >= 10
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# Impossible travel
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index=auth result="success"
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| iplocation src_ip
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| sort user _time
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| streamstats last(lat) as prev_lat last(lon) as prev_lon last(_time) as prev_time by user
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| eval dist=6371*2*asin(sqrt(pow(sin((lat-prev_lat)*pi()/360),2)+cos(prev_lat*pi()/180)*cos(lat*pi()/180)*pow(sin((lon-prev_lon)*pi()/360),2)))
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| eval speed=dist/((_time-prev_time)/3600)
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| where speed > 900 AND dist > 100
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```
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## GeoIP with MaxMind (Python)
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```python
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import geoip2.database
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reader = geoip2.database.Reader('/opt/geoip/GeoLite2-City.mmdb')
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response = reader.city('203.0.113.50')
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print(response.city.name, response.location.latitude, response.location.longitude)
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reader.close()
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```
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## Isolation Forest (scikit-learn)
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```python
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from sklearn.ensemble import IsolationForest
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model = IsolationForest(n_estimators=200, contamination=0.01, random_state=42)
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model.fit(X)
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predictions = model.predict(X) # -1 = anomaly, 1 = normal
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scores = model.score_samples(X) # lower = more anomalous
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```
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