Analyzing Network Traffic Of Malware
Analyzes network traffic generated by malware during sandbox execution
- Skill ID
- mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware
- Publisher
- mukul975
- Repository
- anthropic-cybersecurity-skills
- Installs
- 0
- Files
- 4
- License
- Apache-2.0
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malwareInstalls these files- LICENSE
- SKILL.md
- references/api-reference.md
- scripts/agent.py
What this skill tells the agent
Analyzing Network Traffic of Malware
When to Use
- Sandbox execution has captured a PCAP file and the network behavior needs detailed analysis
- Identifying the C2 protocol structure for writing network detection signatures
- Determining what data the malware exfiltrates and to which external infrastructure
- Analyzing DNS tunneling, domain generation algorithms (DGA), or fast-flux behavior
- Creating Suricata/Snort signatures based on observed malware network patterns
Do not use for host-based analysis of malware behavior; use Cuckoo sandbox reports or Volatility memory analysis for process-level activity.
Prerequisites
- Wireshark 4.x installed for interactive PCAP analysis
- tshark (Wireshark CLI) for scripted packet extraction
- Zeek installed for automated metadata generation from PCAPs
- Suricata with ET Open/ET Pro rulesets for signature matching
- NetworkMiner for file extraction and credential detection from PCAPs
- Python 3.8+ with
scapyanddpktfor programmatic packet analysis
Workflow
Step 1: Initial PCAP Overview
Get a high-level understanding of the network traffic:
# Capture statistics
capinfos malware.pcap
# Protocol hierarchy
tshark -r malware.pcap -q -z io,phs
# Endpoint statistics (top talkers)
tshark -r malware.pcap -q -z endpoints,ip
# Conversation statistics
tshark -r malware.pcap -q -z conv,tcp
# DNS query summary
tshark -r malware.pcap -q -z dns,treeStep 2: Analyze DNS Activity
Examine DNS queries for DGA, tunneling, or C2 domain resolution:
# Extract all DNS queries
tshark -r malware.pcap -T fields -e frame.time -e dns.qry.name -e dns.a \
-Y "dns.flags.response == 1" | sort
# Detect DGA patterns (high entropy domain names)
python3 << 'PYEOF'
import math
from collections import Counter
def entropy(s):
p = [n/len(s) for n in Counter(s).values()]
return -sum(pi * math.log2(pi) for pi in p if pi > 0)
# Parse DNS queries from tshark output
import subprocess
result = subprocess.run(
["tshark", "-r", "malware.pcap", "-T", "fields", "-e", "dns.qry.name",
"-Y", "dns.flags.response == 0"],
capture_output=True, text=True
)
domains = set(result.stdout.strip().split('\n'))
print("Suspicious DNS queries (high entropy):")
for domain in domains:
if domain:
subdomain = domain.split('.')[0]
ent = entropy(subdomain)
if ent > 3.5 and len(subdomain) > 10:
print(f" {domain} (entropy: {ent:.2f})")
PYEOF
# Detect DNS tunneling (large TXT responses)
tshark -r malware.pcap -T fields -e dns.qry.name -e dns.txt \
-Y "dns.resp.type == 16 and dns.resp.len > 100"Step 3: Analyze HTTP/HTTPS C2 Communication
Examine web-based command-and-control traffic:
# Extract HTTP requests
tshark -r malware.pcap -T fields \
-e frame.time -e ip.src -e ip.dst -e http.host \
-e http.request.method -e http.request.uri -e http.user_agent \
-Y "http.request"
# Extract HTTP response bodies (potential payload downloads)
tshark -r malware.pcap -T fields \
-e http.host -e http.request.uri -e http.content_type -e tcp.len \
-Y "http.response and tcp.len > 1000"
# Extract POST data (potential exfiltration)
tshark -r malware.pcap -T fields \
-e http.host -e http.request.uri -e http.file_data \
-Y "http.request.method == POST"
# TLS analysis (SNI, JA3 fingerprints)
tshark -r malware.pcap -T fields \
-e tls.handshake.extensions_server_name \
-e tls.handshake.ja3 \
-Y "tls.handshake.type == 1"
# Extract TLS certificate details
tshark -r malware.pcap -T fields \
-e x509ce.dNSName -e x509af.serialNumber \
-e x509sat.utf8String \
-Y "tls.handshake.type == 11"
# Export HTTP objects (downloaded files)
tshark -r malware.pcap --export-objects http,exported_files/Step 4: Detect Beaconing Patterns
Identify regular periodic communication indicating C2 beaconing:
# Beacon detection from PCAP
from scapy.all import rdpcap, IP, TCP
from collections import defaultdict
import statistics
packets = rdpcap("malware.pcap")
# Group connections by destination IP:port
connections = defaultdict(list)
for pkt in packets:
if IP in pkt and TCP in pkt:
if pkt[TCP].flags & 0x02: # SYN flag
dst = f"{pkt[IP].dst}:{pkt[TCP].dport}"
connections[dst].append(float(pkt.time))
# Analyze timing intervals for beaconing
print("Beacon Analysis:")
for dst, times in connections.items():
if len(times) >= 5:
intervals = [times[i+1] - times[i] for i in range(len(times)-1)]
avg = statistics.mean(intervals)
stdev = statistics.stdev(intervals) if len(intervals) > 1 else 0
jitter = (stdev / avg * 100) if avg > 0 else 0
if 10 < avg < 3600 and jitter < 30: # Regular interval with < 30% jitter
print(f" [!] {dst}: {len(times)} connections")
print(f" Interval: {avg:.1f}s ± {stdev:.1f}s (jitter: {jitter:.1f}%)")
print(f" Pattern: LIKELY BEACONING")Step 5: Generate Network Detection Signatures
Create Suricata/Snort rules from observed traffic patterns:
# Run Suricata against the PCAP for existing signature matches
suricata -r malware.pcap -l suricata_output/ -c /etc/suricata/suricata.yaml
# Review alerts
cat suricata_output/fast.log
# Create custom Suricata rule from observed patterns
cat << 'EOF' > custom_malware.rules
# C2 beacon detection based on observed URI pattern
alert http $HOME_NET any -> $EXTERNAL_NET any (
msg:"MALWARE MalwareX C2 Beacon";
flow:established,to_server;
http.method; content:"POST";
http.uri; content:"/gate.php?id=";
http.user_agent; content:"Mozilla/5.0 (compatible; MSIE 10.0)";
sid:9000001; rev:1;
)
# DNS query for known C2 domain
alert dns $HOME_NET any -> any any (
msg:"MALWARE MalwareX C2 DNS Query";
dns.query; content:"update.malicious.com";
sid:9000002; rev:1;
)
# JA3 hash match for malware TLS client
alert tls $HOME_NET any -> $EXTERNAL_NET any (
msg:"MALWARE MalwareX JA3 Match";
ja3.hash; content:"a0e9f5d64349fb13191bc781f81f42e1";
sid:9000003; rev:1;
)
EOFStep 6: Extract Files and Artifacts from Traffic
Recover transferred files and embedded data:
# Extract files using Zeek
zeek -r malware.pcap /opt/zeek/share/zeek/policy/frameworks/files/extract-all-files.zeek
ls extract_files/
# Extract files using NetworkMiner (GUI)
# Or use tshark for specific protocol exports
tshark -r malware.pcap --export-objects http,http_objects/
tshark -r malware.pcap --export-objects smb,smb_objects/
tshark -r malware.pcap --export-objects tftp,tftp_objects/
# Hash all extracted files
sha256sum http_objects/* smb_objects/* 2>/dev/null
# Generate Zeek logs for comprehensive metadata
zeek -r malware.pcap
# Output: conn.log, dns.log, http.log, ssl.log, files.log, etc.Key Concepts
| Term | Definition |
|---|---|
| Beaconing | Regular periodic connections from malware to C2 server, identifiable by consistent time intervals and packet sizes |
| JA3/JA3S | TLS fingerprinting method creating a hash from ClientHello/ServerHello parameters to uniquely identify malware TLS implementations |
| DGA (Domain Generation Algorithm) | Algorithm generating pseudo-random domain names that malware queries to locate C2 servers, evading static domain blocklists |
| DNS Tunneling | Encoding data in DNS queries and responses to establish a C2 channel or exfiltrate data through DNS infrastructure |
| Fast Flux | DNS technique rapidly rotating IP addresses for a domain to avoid takedown and distribute C2 across many compromised hosts |
| SNI (Server Name Indication) | TLS extension revealing the hostname the client is connecting to; visible even in encrypted HTTPS connections |
| Network Signature | Suricata/Snort rule matching specific patterns in network traffic (headers, payloads, timing) to detect malicious communications |
Tools & Systems
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