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 scapy and dpkt for 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,tree

Step 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:

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