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Detects and analyzes covert communication channels in malware including DNS tunneling, ICMP exfiltration, and HTTP steganography for C2 and data exfiltration analysis.
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/cybersecurity-skills:analyzing-network-covert-channels-in-malwareThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Malware uses covert channels to disguise C2 communication and data exfiltration within legitimate-looking network traffic. DNS tunneling encodes data in DNS queries and responses (used by tools like iodine, dnscat2, and malware families like FrameworkPOS). ICMP tunneling hides data in echo request/reply payloads (icmpsh, ptunnel). HTTP covert channels embed C2 data in headers, cookies, or stega...
Malware uses covert channels to disguise C2 communication and data exfiltration within legitimate-looking network traffic. DNS tunneling encodes data in DNS queries and responses (used by tools like iodine, dnscat2, and malware families like FrameworkPOS). ICMP tunneling hides data in echo request/reply payloads (icmpsh, ptunnel). HTTP covert channels embed C2 data in headers, cookies, or steganographic images. Protocol abuse exploits allowed protocols to bypass firewalls. DNS tunneling detection achieves 99%+ recall with modern ML-based approaches, though low-throughput exfiltration remains challenging. Palo Alto Unit42 tracked three major DNS tunneling campaigns (TrkCdn, SecShow, Savvy Seahorse) through 2024, showing the technique's continued prevalence.
scapy, dpkt, dnslib#!/usr/bin/env python3
"""Detect DNS tunneling and covert channels in network traffic."""
import sys
import json
import math
from collections import Counter, defaultdict
try:
from scapy.all import rdpcap, DNS, DNSQR, DNSRR, IP, ICMP
except ImportError:
print("pip install scapy")
sys.exit(1)
def entropy(data):
if not data:
return 0
freq = Counter(data)
length = len(data)
return -sum((c/length) * math.log2(c/length) for c in freq.values())
def analyze_dns_tunneling(pcap_path):
"""Detect DNS tunneling indicators in PCAP."""
packets = rdpcap(pcap_path)
domain_stats = defaultdict(lambda: {
"queries": 0, "total_qname_len": 0, "subdomain_lengths": [],
"query_types": Counter(), "unique_subdomains": set(),
})
for pkt in packets:
if pkt.haslayer(DNS) and pkt.haslayer(DNSQR):
qname = pkt[DNSQR].qname.decode('utf-8', errors='replace').rstrip('.')
qtype = pkt[DNSQR].qtype
parts = qname.split('.')
if len(parts) >= 3:
base_domain = '.'.join(parts[-2:])
subdomain = '.'.join(parts[:-2])
stats = domain_stats[base_domain]
stats["queries"] += 1
stats["total_qname_len"] += len(qname)
stats["subdomain_lengths"].append(len(subdomain))
stats["query_types"][qtype] += 1
stats["unique_subdomains"].add(subdomain)
# Score domains for tunneling indicators
suspicious = []
for domain, stats in domain_stats.items():
if stats["queries"] < 5:
continue
avg_subdomain_len = (sum(stats["subdomain_lengths"]) /
len(stats["subdomain_lengths"]))
unique_ratio = len(stats["unique_subdomains"]) / stats["queries"]
# Calculate subdomain entropy
all_subdomains = ''.join(stats["unique_subdomains"])
sub_entropy = entropy(all_subdomains)
score = 0
reasons = []
if avg_subdomain_len > 30:
score += 30
reasons.append(f"Long subdomains (avg {avg_subdomain_len:.0f} chars)")
if unique_ratio > 0.9:
score += 25
reasons.append(f"High uniqueness ({unique_ratio:.2%})")
if sub_entropy > 4.0:
score += 25
reasons.append(f"High entropy ({sub_entropy:.2f})")
if stats["query_types"].get(16, 0) > 10: # TXT records
score += 20
reasons.append(f"Many TXT queries ({stats['query_types'][16]})")
if score >= 50:
suspicious.append({
"domain": domain,
"score": score,
"queries": stats["queries"],
"avg_subdomain_length": round(avg_subdomain_len, 1),
"unique_subdomains": len(stats["unique_subdomains"]),
"subdomain_entropy": round(sub_entropy, 2),
"reasons": reasons,
})
return sorted(suspicious, key=lambda x: -x["score"])
def analyze_icmp_tunneling(pcap_path):
"""Detect ICMP tunneling in PCAP."""
packets = rdpcap(pcap_path)
icmp_stats = defaultdict(lambda: {"count": 0, "payload_sizes": [], "payloads": []})
for pkt in packets:
if pkt.haslayer(ICMP) and pkt.haslayer(IP):
src = pkt[IP].src
dst = pkt[IP].dst
key = f"{src}->{dst}"
payload = bytes(pkt[ICMP].payload)
icmp_stats[key]["count"] += 1
icmp_stats[key]["payload_sizes"].append(len(payload))
if len(payload) > 64:
icmp_stats[key]["payloads"].append(payload[:100])
suspicious = []
for flow, stats in icmp_stats.items():
if stats["count"] < 5:
continue
avg_size = sum(stats["payload_sizes"]) / len(stats["payload_sizes"])
if avg_size > 64 or stats["count"] > 100:
suspicious.append({
"flow": flow,
"packets": stats["count"],
"avg_payload_size": round(avg_size, 1),
"reason": "Large/frequent ICMP payloads suggest tunneling",
})
return suspicious
if __name__ == "__main__":
if len(sys.argv) < 2:
print(f"Usage: {sys.argv[0]} <pcap_file>")
sys.exit(1)
print("[+] DNS Tunneling Analysis")
dns_results = analyze_dns_tunneling(sys.argv[1])
for r in dns_results:
print(f" {r['domain']} (score: {r['score']})")
for reason in r['reasons']:
print(f" - {reason}")
print("\n[+] ICMP Tunneling Analysis")
icmp_results = analyze_icmp_tunneling(sys.argv[1])
for r in icmp_results:
print(f" {r['flow']}: {r['reason']}")
npx claudepluginhub costrict-plugins-repo/mukul975-anthropic-cybersecurity-skills-cybersecurity-skillsDetects and analyzes malware covert channels including DNS tunneling, ICMP exfiltration, steganographic HTTP, and protocol abuse for C2 and data exfiltration. For incident response and threat hunting.
Detects and analyzes covert network channels in malware including DNS tunneling, ICMP exfiltration, and steganographic HTTP. Provides Python-based detection scripts and structured workflows for SOC analysts.
Detects and analyzes covert network channels in malware including DNS tunneling, ICMP exfiltration, and steganographic HTTP. Provides Python-based detection scripts and structured workflows for SOC analysts.