Individual skill
detecting-beaconing-patterns-with-zeek
Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.
internal/tools/skills/data/security-operations/detecting-beaconing-patterns-with-zeek/SKILL.md
Source commit d42f381ad34843e71a42658e5c57290c713e85af
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Instructions only
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,325
- Repository last updated
- Nov 9, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- cybersecurity Repository topic
- security Repository topic
- bug-bounty Repository topic
- automation Repository topic
- autonomous-pentesting Repository topic
- ai-agent Repository topic
- ai-security Repository topic
- ethical-hacking Repository topic
- golang Repository topic
- penetration-testing Repository topic
- detecting Declared skill tag
- beaconing Declared skill tag
- patterns Declared skill tag
- with Declared skill tag
- data Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# Detecting Beaconing Patterns with Zeek
## When to Use
- When investigating security incidents that require detecting beaconing patterns with zeek
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
## Detection Gaps & Validation
- **Jitter defeats low-std-dev rules:** modern C2 (Cobalt Strike `jitter`, Sliver, malleable profiles) randomizes sleep ±20–50%, inflating the standard deviation so a raw `std_dev`-only test misses it. Use the coefficient of variation (CV = std/mean) and also bucket inter-arrival times into a histogram / FFT to find a dominant period despite jitter; flag CV below ~0.1–0.3 rather than near-zero.
- **Too few samples = false negative:** the `len(intervals) > 10` gate silently skips slow beacons (hourly/daily callbacks) over short capture windows. Beaconing detection needs long-haul conn.log data (24h+); state the window and don't treat short captures as clean.