npx skills add mukul975/Anthropic-Cybersecurity-SkillsMITRE ATT&CK
NIST CSF 2.0
MITRE D3FEND
Overview
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.
When to Use
- When investigating security incidents that require analyzing network covert channels in malware
- 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
- Python 3.9+ with
scapy,dpkt,dnslib - Wireshark/tshark for PCAP analysis
- Zeek (formerly Bro) for network monitoring
- DNS query logging infrastructure
- Understanding of DNS, ICMP, HTTP protocols at packet level
Workflow
Step 1: DNS Tunneling Detection
#!/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']}")Validation Criteria
- DNS tunneling detected via entropy, subdomain length, and query volume analysis
- ICMP covert channels identified through payload size anomalies
- Tunneling domains distinguished from legitimate CDN/cloud traffic
- Data exfiltration volume estimated from captured traffic
- C2 communication patterns and beaconing intervals extracted
References
References and resources
Everything below is rendered for inspection. Script files are read-only and never run.
References 3
api-reference.md2.5 KB
API Reference: Network Covert Channel Detection
Scapy - Packet Analysis
DNS Tunneling Detection
from scapy.all import rdpcap, DNS, DNSQR, IP
packets = rdpcap("capture.pcap")
for pkt in packets:
if pkt.haslayer(DNSQR):
qname = pkt[DNSQR].qname.decode().rstrip(".")
src = pkt[IP].src
qtype = pkt[DNSQR].qtype # 1=A, 16=TXT, 28=AAAAICMP Payload Extraction
from scapy.all import ICMP, Raw
for pkt in packets:
if pkt.haslayer(ICMP) and pkt.haslayer(Raw):
payload = bytes(pkt[Raw].load)
icmp_type = pkt[ICMP].type # 8=echo-request, 0=echo-replyZeek - Covert Channel Detection
DNS Tunneling Indicators
@load base/protocols/dns
event dns_request(c: connection, msg: dns_msg, query: string, qtype: count) {
if (|query| > 60)
print fmt("Long DNS query: %s from %s", query, c$id$orig_h);
}Configuration
zeek -r capture.pcap local
# Outputs: dns.log, conn.log, weird.logtshark - Protocol Filtering
DNS Analysis
tshark -r capture.pcap -Y "dns" -T fields \
-e ip.src -e dns.qry.name -e dns.qry.type -e frame.len
# Filter long DNS queries
tshark -r capture.pcap -Y "dns.qry.name matches \"^.{60,}\"" -T fields -e dns.qry.nameICMP Payload Analysis
tshark -r capture.pcap -Y "icmp && data.len > 64" -T fields \
-e ip.src -e ip.dst -e icmp.type -e data.len -e data.dataDNS Tunneling Tools
| Tool | Technique | Detection Method |
|---|---|---|
| iodine | TXT/NULL/CNAME records | High entropy subdomains |
| dns2tcp | TXT records | Encoded query names |
| dnscat2 | TXT/CNAME/MX/A records | Base32/Base64 subdomain patterns |
| DNSExfiltrator | TXT records | High query volume to single domain |
Entropy Thresholds
| Range | Interpretation |
|---|---|
| < 2.0 | Normal domain labels (English words) |
| 2.0-3.5 | Possibly encoded but may be legitimate |
| 3.5-5.0 | Likely Base32/Base64 encoded (tunneling) |
| > 5.0 | Encrypted/random data (strong tunneling indicator) |
Covert Channel Categories
| Channel Type | Protocol | Detection Method |
|---|---|---|
| DNS Tunneling | DNS (53/udp) | Subdomain entropy, query volume |
| ICMP Tunnel | ICMP (type 8/0) | Payload size, entropy, volume |
| HTTP Header | HTTP (80/tcp) | Cookie size, custom header entropy |
| Protocol Abuse | IP options, GRE | Unusual protocol numbers |
| Timing Channel | TCP | Inter-packet timing analysis |
standards.md0.3 KB
Standards Reference - analyzing-network-covert-channels-in-malware
Applicable Standards
- MITRE ATT&CK Framework
- NIST SP 800-83 Guide to Malware Incident Prevention
- NIST SP 800-86 Guide to Integrating Forensic Techniques
Related MITRE ATT&CK Techniques
See SKILL.md for specific technique mappings.
workflows.md0.5 KB
Analysis Workflows - analyzing-network-covert-channels-in-malware
Primary Workflow
[Sample Collection] --> [Static Analysis] --> [Dynamic Analysis] --> [IOC Extraction]
|
v
[Report Generation]See SKILL.md for detailed step-by-step procedures.
Scripts 1
agent.py7.6 KB
#!/usr/bin/env python3
"""Network covert channel detection agent for malware traffic analysis.
Detects DNS tunneling, ICMP covert channels, HTTP header steganography,
and protocol abuse in PCAP captures using scapy.
"""
import os
import sys
import json
import math
from collections import Counter, defaultdict
try:
from scapy.all import rdpcap, DNS, DNSQR, ICMP, IP, TCP, Raw
HAS_SCAPY = True
except ImportError:
HAS_SCAPY = False
def shannon_entropy(data):
"""Calculate Shannon entropy of byte data."""
if not data:
return 0.0
freq = Counter(data)
length = len(data)
return -sum((c / length) * math.log2(c / length) for c in freq.values())
def detect_dns_tunneling(packets, entropy_threshold=3.5, length_threshold=50):
"""Detect DNS tunneling by analyzing query name entropy and length."""
findings = []
dns_queries = defaultdict(list)
for pkt in packets:
if pkt.haslayer(DNSQR):
qname = pkt[DNSQR].qname.decode("utf-8", errors="replace").rstrip(".")
src = pkt[IP].src if pkt.haslayer(IP) else "?"
labels = qname.split(".")
subdomain = ".".join(labels[:-2]) if len(labels) > 2 else qname
entropy = shannon_entropy(subdomain.encode())
base_domain = ".".join(labels[-2:]) if len(labels) >= 2 else qname
dns_queries[base_domain].append({
"query": qname, "src": src, "entropy": round(entropy, 3),
"subdomain_len": len(subdomain),
})
if entropy > entropy_threshold and len(subdomain) > length_threshold:
findings.append({
"type": "dns_tunneling", "query": qname, "src": src,
"entropy": round(entropy, 3),
"subdomain_length": len(subdomain), "severity": "HIGH",
})
volume_findings = []
for domain, queries in dns_queries.items():
if len(queries) > 100:
avg_entropy = sum(q["entropy"] for q in queries) / len(queries)
if avg_entropy > 3.0:
volume_findings.append({
"type": "dns_high_volume", "domain": domain,
"query_count": len(queries),
"avg_entropy": round(avg_entropy, 3), "severity": "HIGH",
})
return findings[:50], volume_findings
def detect_icmp_covert_channel(packets, payload_threshold=64):
"""Detect ICMP covert channels via payload analysis."""
findings = []
icmp_flows = defaultdict(list)
for pkt in packets:
if pkt.haslayer(ICMP) and pkt.haslayer(Raw):
payload = bytes(pkt[Raw].load)
src = pkt[IP].src if pkt.haslayer(IP) else "?"
dst = pkt[IP].dst if pkt.haslayer(IP) else "?"
entropy = shannon_entropy(payload)
flow_key = f"{src}->{dst}"
icmp_flows[flow_key].append(payload)
if len(payload) > payload_threshold and entropy > 5.0:
findings.append({
"type": "icmp_covert", "src": src, "dst": dst,
"icmp_type": pkt[ICMP].type,
"payload_size": len(payload),
"entropy": round(entropy, 3), "severity": "HIGH",
})
for flow, payloads in icmp_flows.items():
total_bytes = sum(len(p) for p in payloads)
if total_bytes > 10000:
findings.append({
"type": "icmp_exfiltration", "flow": flow,
"total_bytes": total_bytes,
"packet_count": len(payloads), "severity": "HIGH",
})
return findings[:50]
def detect_http_header_covert(packets):
"""Detect covert data in HTTP headers."""
findings = []
for pkt in packets:
if pkt.haslayer(TCP) and pkt.haslayer(Raw):
try:
payload = bytes(pkt[Raw].load).decode("utf-8", errors="replace")
except Exception:
continue
if not payload.startswith(("GET ", "POST ", "HTTP/")):
continue
for line in payload.split("\r\n"):
if ":" not in line:
continue
header, _, value = line.partition(":")
value = value.strip()
if header.lower() == "cookie" and len(value) > 500:
entropy = shannon_entropy(value.encode())
if entropy > 4.5:
findings.append({
"type": "http_cookie_exfil", "header": header,
"value_length": len(value),
"entropy": round(entropy, 3), "severity": "MEDIUM",
})
if header.lower().startswith("x-") and len(value) > 100:
entropy = shannon_entropy(value.encode())
if entropy > 4.0:
findings.append({
"type": "http_custom_header", "header": header,
"value_length": len(value),
"entropy": round(entropy, 3), "severity": "MEDIUM",
})
return findings[:50]
def detect_protocol_anomalies(packets):
"""Detect protocol-level anomalies indicating covert communication."""
findings = []
for pkt in packets:
if pkt.haslayer(IP):
proto = pkt[IP].proto
if proto not in (1, 6, 17, 47, 50, 51):
findings.append({
"type": "unusual_ip_proto", "protocol": proto,
"src": pkt[IP].src, "dst": pkt[IP].dst, "severity": "MEDIUM",
})
return findings[:50]
def generate_report(pcap_path, dns_f, dns_v, icmp_f, http_f, proto_f):
"""Generate covert channel analysis report."""
total = len(dns_f) + len(icmp_f) + len(http_f) + len(proto_f)
return {
"pcap_file": pcap_path, "total_findings": total,
"dns_tunneling": {"count": len(dns_f), "findings": dns_f[:10]},
"dns_volume_anomalies": dns_v[:10],
"icmp_covert": {"count": len(icmp_f), "findings": icmp_f[:10]},
"http_header_covert": {"count": len(http_f), "findings": http_f[:10]},
"protocol_anomalies": {"count": len(proto_f), "findings": proto_f[:10]},
"risk_level": "HIGH" if total > 10 else "MEDIUM" if total > 3 else "LOW",
}
if __name__ == "__main__":
print("=" * 60)
print("Network Covert Channel Detection Agent")
print("DNS tunneling, ICMP covert, HTTP header, protocol abuse")
print("=" * 60)
pcap = sys.argv[1] if len(sys.argv) > 1 else None
if not pcap or not os.path.exists(pcap):
print("\n[DEMO] Usage: python agent.py <capture.pcap>")
print(f" scapy available: {HAS_SCAPY}")
sys.exit(0)
if not HAS_SCAPY:
print("[!] Install scapy: pip install scapy")
sys.exit(1)
print(f"\n[*] Loading: {pcap}")
packets = rdpcap(pcap)
print(f"[*] Packets: {len(packets)}")
dns_f, dns_v = detect_dns_tunneling(packets)
icmp_f = detect_icmp_covert_channel(packets)
http_f = detect_http_header_covert(packets)
proto_f = detect_protocol_anomalies(packets)
report = generate_report(pcap, dns_f, dns_v, icmp_f, http_f, proto_f)
print(f"\n--- DNS Tunneling ({len(dns_f)}) ---")
for f in dns_f[:5]:
print(f" {f['src']} | entropy={f['entropy']} | {f['query'][:60]}")
print(f"\n--- ICMP Covert ({len(icmp_f)}) ---")
for f in icmp_f[:5]:
print(f" {f.get('flow', f.get('src','?'))} | {f.get('payload_size', f.get('total_bytes','?'))}B")
print(f"\n[*] Risk: {report['risk_level']}")
print(json.dumps(report, indent=2, default=str))