external/anthropic-cybersecurity-skills/skills/hunting-for-command-and-control-beaconing/SKILL.md
Detect C2 beaconing patterns in network traffic using frequency analysis, jitter detection, and domain reputation to identify compromised endpoints communicating with adversary infrastructure.
npx skillsauth add seikaikyo/dash-skills hunting-for-command-and-control-beaconingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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| Concept | Description | |---------|-------------| | T1071 | Application Layer Protocol (HTTP/HTTPS/DNS C2) | | T1071.001 | Web Protocols (HTTP/S beaconing) | | T1071.004 | DNS (DNS tunneling C2) | | T1573 | Encrypted Channel | | T1572 | Protocol Tunneling | | T1568 | Dynamic Resolution (DGA, fast-flux) | | T1132 | Data Encoding in C2 | | T1095 | Non-Application Layer Protocol | | Beacon Interval | Time between C2 check-ins | | Jitter | Random variation in beacon interval | | DGA | Domain Generation Algorithm | | Fast-Flux | Rapidly changing DNS resolution |
| Tool | Purpose | |------|---------| | RITA (Real Intelligence Threat Analytics) | Automated beacon detection in Zeek logs | | Splunk | Statistical beacon analysis with SPL | | Elastic Security | ML-based anomaly detection for beaconing | | Zeek/Bro | Network connection metadata collection | | Suricata | Network IDS with JA3/JA4 fingerprinting | | VirusTotal | Domain and IP reputation checking | | PassiveDNS | Historical DNS resolution data | | Flare | C2 profile detection |
Hunt ID: TH-C2-[DATE]-[SEQ]
Source IP: [Internal IP]
Source Host: [Hostname]
Destination: [Domain/IP]
Protocol: [HTTP/HTTPS/DNS/Custom]
Beacon Interval: [Average seconds]
Jitter: [Percentage]
Connection Count: [Total connections]
Data Volume: [Bytes sent/received]
First Seen: [Timestamp]
Last Seen: [Timestamp]
Domain Age: [Days]
TI Match: [Yes/No - source]
Risk Level: [Critical/High/Medium/Low]
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.