Hunting for Cobalt Strike Beacons
Overview
Cobalt Strike is the most prevalent command-and-control framework used by both red teams and threat actors. Beacon, its primary payload, communicates with team servers using configurable HTTP/HTTPS/DNS profiles that can mimic legitimate traffic. However, default configurations and behavioral patterns remain detectable through TLS certificate analysis (default serial 8BB00EE), JA3/JA3S fingerprinting, beacon interval jitter analysis, and HTTP malleable profile pattern matching. This skill covers building detection capabilities using Zeek network logs, Suricata IDS rules, and Python-based PCAP analysis to identify beacon callbacks in network traffic.
When to Use
- When investigating security incidents that require hunting for cobalt strike beacons
- 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
Prerequisites
- Zeek 6.0+ with JA3 and HASSH packages installed
- Suricata 7.0+ with Emerging Threats ruleset
- Python 3.9+ with scapy and dpkt libraries
- Network traffic captures (PCAP) or live Zeek logs
- RITA (Real Intelligence Threat Analytics) for beacon scoring
- Threat intelligence feeds with known Cobalt Strike IOCs
Steps
Step 1: TLS Certificate Analysis
Detect default Cobalt Strike certificates using JA3S fingerprints, certificate serial numbers, and JARM fingerprints in Zeek ssl.log.
Step 2: Beacon Interval Analysis
Analyze connection timing patterns to identify regular callback intervals with configurable jitter, characteristic of beacon behavior.
Step 3: HTTP Profile Detection
Match HTTP request patterns (URI paths, headers, user-agents) against known malleable C2 profiles.
Step 4: Correlate and Score
Combine multiple indicators (TLS + timing + HTTP profile) into a composite beacon confidence score.
Expected Output
JSON report containing detected beacon candidates with confidence scores, TLS fingerprints, timing analysis, HTTP profile matches, and recommended response actions.
---
name: hunting-for-cobalt-strike-beacons
description: Detect Cobalt Strike beacon network activity using TLS certificate signatures, JA3/JA3S/JARM fingerprints, HTTP C2 profile matching, beacon jitter analysis, and named pipe detection via Zeek, Suricata, and Python PCAP analysis.
license: Apache-2.0
---
# Hunting for Cobalt Strike Beacons
## Overview
Cobalt Strike is the most prevalent command-and-control framework used by both red teams and threat actors. Beacon, its primary payload, communicates with team servers using configurable HTTP/HTTPS/DNS profiles that can mimic legitimate traffic. However, default configurations and behavioral patterns remain detectable through TLS certificate analysis (default serial 8BB00EE), JA3/JA3S fingerprinting, beacon interval jitter analysis, and HTTP malleable profile pattern matching. This skill covers building detection capabilities using Zeek network logs, Suricata IDS rules, and Python-based PCAP analysis to identify beacon callbacks in network traffic.
## When to Use
- When investigating security incidents that require hunting for cobalt strike beacons
- 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
## Prerequisites
- Zeek 6.0+ with JA3 and HASSH packages installed
- Suricata 7.0+ with Emerging Threats ruleset
- Python 3.9+ with scapy and dpkt libraries
- Network traffic captures (PCAP) or live Zeek logs
- RITA (Real Intelligence Threat Analytics) for beacon scoring
- Threat intelligence feeds with known Cobalt Strike IOCs
## Steps
### Step 1: TLS Certificate Analysis
Detect default Cobalt Strike certificates using JA3S fingerprints, certificate serial numbers, and JARM fingerprints in Zeek ssl.log.
### Step 2: Beacon Interval Analysis
Analyze connection timing patterns to identify regular callback intervals with configurable jitter, characteristic of beacon behavior.
### Step 3: HTTP Profile Detection
Match HTTP request patterns (URI paths, headers, user-agents) against known malleable C2 profiles.
### Step 4: Correlate and Score
Combine multiple indicators (TLS + timing + HTTP profile) into a composite beacon confidence score.
## Expected Output
JSON report containing detected beacon candidates with confidence scores, TLS fingerprints, timing analysis, HTTP profile matches, and recommended response actions.
