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https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
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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)
83 lines
2.4 KiB
Markdown
83 lines
2.4 KiB
Markdown
---
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name: performing-dns-tunneling-detection
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description: 'Detects DNS tunneling by computing Shannon entropy of DNS query names, analyzing query length distributions,
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inspecting TXT record payloads, and identifying high subdomain cardinality. Uses scapy for packet capture analysis and statistical
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methods to distinguish legitimate DNS from covert channels. Use when hunting for data exfiltration.
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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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- performing
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- dns
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- tunneling
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- detection
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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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# Performing DNS Tunneling Detection
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## When to Use
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- When conducting security assessments that involve performing dns tunneling detection
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- When following incident response procedures for related security events
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- When performing scheduled security testing or auditing activities
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- When validating security controls through hands-on testing
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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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Analyze DNS traffic for indicators of DNS tunneling using entropy analysis and
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statistical methods on query name characteristics.
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```python
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import math
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from collections import Counter
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def shannon_entropy(data):
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if not data:
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return 0
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counter = Counter(data)
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length = len(data)
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return -sum((c/length) * math.log2(c/length) for c in counter.values())
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# Legitimate domain: low entropy (~3.0-3.5)
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print(shannon_entropy("www.google.com"))
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# DNS tunnel: high entropy (~4.0-5.0)
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print(shannon_entropy("aGVsbG8gd29ybGQ.tunnel.example.com"))
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```
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Key detection indicators:
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1. High Shannon entropy in query names (> 3.5 for subdomain labels)
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2. Unusually long query names (> 50 characters)
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3. High volume of TXT record requests to a single domain
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4. High unique subdomain count per parent domain
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5. Non-standard character distribution in labels
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## Examples
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```python
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from scapy.all import rdpcap, DNS, DNSQR
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packets = rdpcap("dns_traffic.pcap")
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for pkt in packets:
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if pkt.haslayer(DNSQR):
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query = pkt[DNSQR].qname.decode()
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entropy = shannon_entropy(query)
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if entropy > 4.0:
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print(f"Suspicious: {query} (entropy={entropy:.2f})")
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```
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