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Analyzing Network Covert Channels In Malware

mukul975/anthropic-cybersecurity-skills

  • 300 installs
  • 27.3k repo stars
  • Updated August 2, 2026
  • mukul975/anthropic-cybersecurity-skills

Analyzing Network Covert Channels in Malware is an agent skill that turns covert-channel malware analysis into a structured report with findings, IOCs, and recommendations.

About

Analyzing Network Covert Channels in Malware is an agent skill for solo and indie security-minded builders who need a consistent written artifact after digging into suspicious binaries or traffic. The packaged SKILL content centers on a formal Analysis Report Template: you capture sample identifiers, document findings with severity, extract indicators of compromise in tabular form, and list prioritized recommendations—matching how small teams share limited-access (TLP:AMBER) research without ad-hoc chat logs. Use it when your agent has already performed or summarized technical analysis of covert C2 patterns (DNS tunneling, protocol steganography, timing channels) and you want the output normalized for reviewers, clients, or your own incident notes. It does not replace disassemblers, sandboxes, or PCAP tools; it standardizes the narrative layer so Ship-phase security reviews stay comparable across engagements. Confidence is moderate because the ingested readme emphasizes the template scaffold rather than step-by-step detection recipes—pair with your lab toolchain and human judgment on attribution and disclosure.

  • TLP:AMBER-styled analysis report template with sample metadata table (SHA-256, file type, analyst, date)
  • Findings matrix with Severity and Details columns for structured triage
  • Dedicated IOC extraction table (Type, Value, Context) for threat-intel handoff
  • Numbered recommendations section for post-analysis action items
  • Apache 2.0 licensed skill package from anthropic-cybersecurity-skills collection

Analyzing Network Covert Channels In Malware by the numbers

  • 300 all-time installs (skills.sh)
  • +17 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #637 of 2,203 Security skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-network-covert-channels-in-malware

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Listed on Skillselion
Installs300
repo stars★ 27.3k
Security audit3 / 3 scanners passed
Last updatedAugust 2, 2026
Repositorymukul975/anthropic-cybersecurity-skills ↗

What it does

Structure malware reverse-engineering notes on network covert channels into a TLP-style analysis report with findings, IOCs, and remediation steps.

Who is it for?

Developers or one-person security consultancies documenting malware covert-channel analysis after lab or sandbox work.

Skip if: Skip if you need automated PCAP parsing, YARA generation, or full MITRE ATT&CK mapping without supplying your own analysis content—the skill is primarily a reporting scaffold.

When should I use this skill?

After completing or summarizing technical analysis of network covert channels in malware and you need a standardized security report.

What you get

You get a filled Analysis Report Template with severity-ranked findings, extracted IOCs, and numbered recommendations ready for review or incident follow-up.

  • Completed Analysis Report Template with findings and IOC tables
  • Prioritized recommendations list for remediation or further hunting

By the numbers

  • Report template includes 3 numbered recommendation slots
  • Three core tables: Sample Information, Findings (severity), IOCs Extracted

Files

SKILL.mdMarkdownGitHub ↗

Analyzing Network Covert Channels in Malware

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

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How it compares

Use as a reporting template after technical analysis, not as a substitute for interactive malware sandboxes or generic debugging skills.

FAQ

Who is analyzing-network-covert-channels-in-malware for?

Developers, founders doing security reviews, and agent users documenting malware network covert-channel findings in a consistent report format.

When should I use analyzing-network-covert-channels-in-malware?

During Ship security reviews after you have analysis results to formalize, or in Operate when investigating suspected covert C2 in software you ship or run.

Is analyzing-network-covert-channels-in-malware safe to install?

Treat it like any third-party skill: review the Security Audits panel on this Prism page and your org policy before enabling shell-capable agents on sensitive malware artifacts.

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