mirror of
https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
synced 2026-09-02 22:50:49 +03:00
feat: add 4 new cybersecurity skills - UEBA insider threat, BeyondCorp zero trust, Linux kernel rootkits, CobaltStrike beacon hunting
This commit is contained in:
@@ -0,0 +1,75 @@
|
||||
# API Reference: Detecting Insider Threat with UEBA
|
||||
|
||||
## Elasticsearch Aggregation Queries
|
||||
|
||||
### Per-User Daily Activity Baseline
|
||||
```json
|
||||
{
|
||||
"aggs": {
|
||||
"users": {
|
||||
"terms": {"field": "user.name", "size": 5000},
|
||||
"aggs": {
|
||||
"daily_events": {"date_histogram": {"field": "@timestamp", "calendar_interval": "day"}},
|
||||
"unique_hosts": {"cardinality": {"field": "host.name"}},
|
||||
"data_volume": {"sum": {"field": "bytes_transferred"}}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Anomaly Detection (Z-Score > 3)
|
||||
```python
|
||||
from elasticsearch import Elasticsearch
|
||||
es = Elasticsearch(["https://localhost:9200"], api_key="base64key")
|
||||
result = es.search(index="logs-*", body=query)
|
||||
z_score = (current - baseline_avg) / baseline_std
|
||||
```
|
||||
|
||||
## Insider Threat Indicators
|
||||
|
||||
| Indicator | Detection Method | Severity |
|
||||
|-----------|-----------------|----------|
|
||||
| Activity spike | Z-score > 3 standard deviations | High |
|
||||
| Data exfiltration | Volume > 5x daily average | Critical |
|
||||
| New host access | Unique hosts > 2x baseline | High |
|
||||
| Off-hours activity | Login outside 06:00-22:00 | Medium |
|
||||
| Peer group outlier | Activity > 3x peer average | Medium |
|
||||
| Privilege escalation | New admin role assignment | Critical |
|
||||
| Resignation + download | HR flag + high data volume | Critical |
|
||||
|
||||
## Elasticsearch Python Client
|
||||
|
||||
```bash
|
||||
pip install elasticsearch>=8.0
|
||||
```
|
||||
|
||||
| Method | Description |
|
||||
|--------|-------------|
|
||||
| `es.search(index, body)` | Execute aggregation query |
|
||||
| `es.indices.get_alias("logs-*")` | List matching indices |
|
||||
| `es.count(index)` | Get document count |
|
||||
|
||||
## Risk Scoring Model
|
||||
|
||||
| Score Range | Risk Level | Action |
|
||||
|-------------|------------|--------|
|
||||
| 0 - 30 | Low | No action |
|
||||
| 31 - 60 | Medium | Monitor |
|
||||
| 61 - 80 | High | SOC investigation |
|
||||
| 81 - 100 | Critical | Immediate response |
|
||||
|
||||
## MITRE ATT&CK Insider Techniques
|
||||
|
||||
| Technique | ID | UEBA Detection |
|
||||
|-----------|----|----------------|
|
||||
| Data from Local System | T1005 | Volume anomaly on file servers |
|
||||
| Exfiltration Over Web Service | T1567 | Cloud upload volume spike |
|
||||
| Account Manipulation | T1098 | Unusual privilege changes |
|
||||
| Valid Accounts | T1078 | Off-hours or location anomaly |
|
||||
|
||||
### References
|
||||
|
||||
- Elasticsearch Python Client: https://elasticsearch-py.readthedocs.io/
|
||||
- MITRE Insider Threat: https://attack.mitre.org/techniques/T1078/
|
||||
- NIST SP 800-53 AC-2: https://csf.tools/reference/nist-sp-800-53/r5/ac/ac-2/
|
||||
Reference in New Issue
Block a user