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mukul975
efca3ec611
Mapped every skill to NIST CSF 2.0 subcategory IDs (GV/ID/PR/DE/RS/RC functions) based on subdomain and content analysis. Restores 11 skills corrupted during prior rebase, re-enriching with ATLAS, D3FEND, NIST AI RMF, and CSF 2.0 fields. All 754 skills now carry structured mappings for all 5 security frameworks: - MITRE ATT&CK (in tags) - MITRE ATLAS v5.5 (atlas_techniques) - MITRE D3FEND v1.3 (d3fend_techniques) - NIST AI RMF 1.0 (nist_ai_rmf) - NIST CSF 2.0 (nist_csf)
79 lines
2.5 KiB
Markdown
79 lines
2.5 KiB
Markdown
---
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name: detecting-beaconing-patterns-with-zeek
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description: 'Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the
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ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic
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connections with low jitter. Use when hunting for command-and-control callbacks in network data.
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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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- detecting
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- beaconing
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- patterns
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- with
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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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---
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# Detecting Beaconing Patterns with Zeek
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## When to Use
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- When investigating security incidents that require detecting beaconing patterns with zeek
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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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Load Zeek conn.log data using ZAT (Zeek Analysis Tools), group connections by
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source/destination pairs, and compute timing statistics to identify beaconing.
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```python
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from zat.log_to_dataframe import LogToDataFrame
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import numpy as np
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log_to_df = LogToDataFrame()
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conn_df = log_to_df.create_dataframe('/path/to/conn.log')
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# Group by src/dst pair and calculate inter-arrival time
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for (src, dst), group in conn_df.groupby(['id.orig_h', 'id.resp_h']):
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times = group['ts'].sort_values()
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intervals = times.diff().dt.total_seconds().dropna()
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if len(intervals) > 10:
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std_dev = np.std(intervals)
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mean_interval = np.mean(intervals)
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# Low std_dev relative to mean = likely beaconing
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```
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Key analysis steps:
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1. Parse Zeek conn.log into DataFrame with ZAT LogToDataFrame
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2. Group connections by source IP and destination IP pairs
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3. Calculate inter-arrival time intervals between consecutive connections
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4. Compute standard deviation and coefficient of variation
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5. Flag pairs with low coefficient of variation as potential beacons
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## Examples
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```python
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from zat.log_to_dataframe import LogToDataFrame
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log_to_df = LogToDataFrame()
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df = log_to_df.create_dataframe('conn.log')
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print(df[['id.orig_h', 'id.resp_h', 'ts', 'duration']].head())
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```
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