Performing Network Packet Capture Analysis

Performing Network Packet Capture Analysis

Overview

Network packet captures (PCAP/PCAPNG files) represent the ultimate source of truth about network activity and provide irrefutable evidence of communications between hosts. PCAP files log every packet transmitted over a network segment, making them vital for forensic investigations involving data exfiltration, command-and-control communications, lateral movement, malware delivery, and unauthorized access. Wireshark is the primary tool for interactive analysis, while tshark provides command-line capabilities for automated processing and scripting. Modern PCAPNG format supports additional metadata including interface descriptions, capture comments, precise timestamps, and per-packet annotations.

When to Use

• When conducting security assessments that involve performing network packet capture analysis
• When following incident response procedures for related security events
• When performing scheduled security testing or auditing activities
• When validating security controls through hands-on testing

Detection Gaps & Validation

A capture is evidence of what the sensor recorded, not of everything that happened. Account for these gaps before drawing conclusions:

• Truncation and packet loss. A small snaplen (tcpdump -s 96) records headers but strips payloads, so tshark --export-objects and Scapy Raw analysis come up empty. Confirm with capinfos (snaplen, drop counts, duration) that the file is full-payload and spans the incident window. Asymmetric/one-sided captures (SPAN misconfig) prevent TCP reassembly, producing false "no data transferred" results.
• Encrypted payloads. TLS 1.3, QUIC, DoH/DoT, and SSH render bodies opaque without keys. Pivot to surviving metadata — SNI (unless ECH), JA3/JA3S, certificate issuer/validity, flow sizes, and beacon timing. Absence of readable content is not absence of malicious activity.
• Beaconing and tunneling need statistics, not a filter. Jittered C2 evades fixed frame.time_delta windows. Compute inter-arrival mean/variance per dst:port over the whole capture (the Python detect_beaconing here), and treat long random DNS labels as candidate tunneling to verify, since CDNs/AV telemetry/analytics produce similar entropy.
• Validate every extracted artifact. Carved/reassembled objects can be partial if retransmissions or segments are missing. Hash each file and check threat intel/VirusTotal before labeling malware, and confirm the transfer actually completed (HTTP 200 + matching content-length) rather than an aborted GET.
• Interpretation false positives. Self-signed certs and odd ports are common on internal/IoT hosts; "top talker" volume is often backup/replication, not exfiltration. Verify the capture host's clock and timezone before building a timeline, and corroborate network findings with endpoint logs (process, DNS, auth) rather than relying on the PCAP alone.

Prerequisites

• Wireshark 4.x with protocol dissectors
• tshark command-line tool (included with Wireshark)
• tcpdump for capture and basic filtering
• Python 3.8+ with scapy and pyshark libraries
• Sufficient disk space for PCAP files (can be multi-GB)

Capture Techniques

tcpdump

# Capture all traffic on interface eth0
tcpdump -i eth0 -w capture.pcap

# Capture with rotation (100MB files, keep 10)
tcpdump -i eth0 -w capture_%Y%m%d_%H%M%S.pcap -C 100 -W 10

# Capture specific host traffic
tcpdump -i eth0 host 192.168.1.100 -w host_traffic.pcap

# Capture specific port traffic
tcpdump -i eth0 port 443 -w https_traffic.pcap

# Capture with BPF filter for suspicious ports
tcpdump -i eth0 'port 4444 or port 8080 or port 1337' -w suspicious.pcap

Wireshark Display Filters

# HTTP traffic
http

# DNS queries
dns

# SMB file transfers
smb2

# Specific IP communication
ip.addr == 192.168.1.100

# Failed TCP connections
tcp.flags.syn == 1 && tcp.flags.ack == 0

# Large data transfers (potential exfiltration)
tcp.len > 1000

# Specific protocol by port
tcp.port == 4444

# TLS handshakes (SNI extraction)
tls.handshake.type == 1

# HTTP POST requests
http.request.method == "POST"

# DNS queries to suspicious TLDs
dns.qry.name contains ".xyz" or dns.qry.name contains ".top"

# Beaconing detection (regular intervals)
frame.time_delta_displayed > 55 && frame.time_delta_displayed < 65

tshark Analysis Commands

# Extract HTTP URLs from capture
tshark -r capture.pcap -Y "http.request" -T fields -e http.host -e http.request.uri

# Extract DNS queries
tshark -r capture.pcap -Y "dns.flags.response == 0" -T fields -e dns.qry.name | sort -u

# Extract file transfers (HTTP objects)
tshark -r capture.pcap --export-objects http,exported_files/

# Extract SMB file transfers
tshark -r capture.pcap --export-objects smb,smb_files/

# Protocol hierarchy statistics
tshark -r capture.pcap -z io,phs

# Conversation statistics
tshark -r capture.pcap -z conv,tcp

# Extract TLS SNI (Server Name Indication)
tshark -r capture.pcap -Y "tls.handshake.type == 1" -T fields -e tls.handshake.extensions_server_name

# Top talkers by bytes
tshark -r capture.pcap -z endpoints,ip -q

# Extract credentials (FTP, HTTP Basic)
tshark -r capture.pcap -Y "ftp.request.command == USER || ftp.request.command == PASS || http.authorization" -T fields -e ftp.request.arg -e http.authorization

Python PCAP Analysis

from scapy.all import rdpcap, IP, TCP, UDP, DNS, DNSQR, Raw
import os
import sys
import json
from collections import defaultdict, Counter
from datetime import datetime


class PCAPForensicAnalyzer:
    """Forensic analysis of PCAP files using Scapy."""

    def __init__(self, pcap_path: str, output_dir: str):
        self.pcap_path = pcap_path
        self.output_dir = output_dir
        os.makedirs(output_dir, exist_ok=True)
        self.packets = rdpcap(pcap_path)

    def get_conversations(self) -> list:
        """Extract unique IP conversations with byte counts."""
        convos = defaultdict(lambda: {"packets": 0, "bytes": 0})
        for pkt in self.packets:
            if IP in pkt:
                key = tuple(sorted([pkt[IP].src, pkt[IP].dst]))
                convos[key]["packets"] += 1
                convos[key]["bytes"] += len(pkt)

        return [
            {"src": k[0], "dst": k[1], "packets": v["packets"], "bytes": v["bytes"]}
            for k, v in sorted(convos.items(), key=lambda x: x[1]["bytes"], reverse=True)
        ]

    def extract_dns_queries(self) -> list:
        """Extract all DNS queries from the capture."""
        queries = []
        for pkt in self.packets:
            if DNS in pkt and pkt[DNS].qr == 0 and DNSQR in pkt:
                queries.append({
                    "query": pkt[DNSQR].qname.decode(errors="replace").rstrip("."),
                    "type": pkt[DNSQR].qtype,
                    "src": pkt[IP].src if IP in pkt else "unknown"
                })
        return queries

    def detect_beaconing(self, threshold_seconds: float = 5.0) -> list:
        """Detect potential beaconing activity based on regular intervals."""
        ip_timestamps = defaultdict(list)
        for pkt in self.packets:
            if IP in pkt and TCP in pkt:
                key = (pkt[IP].src, pkt[IP].dst, pkt[TCP].dport)
                ip_timestamps[key].append(float(pkt.time))

        beacons = []
        for key, times in ip_timestamps.items():
            if len(times) < 5:
                continue
            deltas = [times[i+1] - times[i] for i in range(len(times)-1)]
            if deltas:
                avg_delta = sum(deltas) / len(deltas)
                variance = sum((d - avg_delta) ** 2 for d in deltas) / len(deltas)
                if variance < threshold_seconds and avg_delta > 1:
                    beacons.append({
                        "src": key[0], "dst": key[1], "port": key[2],
                        "avg_interval": round(avg_delta, 2),
                        "variance": round(variance, 4),
                        "connection_count": len(times)
                    })
        return sorted(beacons, key=lambda x: x["variance"])

    def get_protocol_distribution(self) -> dict:
        """Get protocol distribution statistics."""
        protocols = Counter()
        for pkt in self.packets:
            if TCP in pkt:
                protocols[f"TCP/{pkt[TCP].dport}"] += 1
            elif UDP in pkt:
                protocols[f"UDP/{pkt[UDP].dport}"] += 1
        return dict(protocols.most_common(50))

    def generate_report(self) -> str:
        """Generate comprehensive PCAP analysis report."""
        report = {
            "analysis_timestamp": datetime.now().isoformat(),
            "pcap_file": self.pcap_path,
            "total_packets": len(self.packets),
            "conversations": self.get_conversations()[:50],
            "dns_queries": self.extract_dns_queries()[:200],
            "potential_beacons": self.detect_beaconing(),
            "protocol_distribution": self.get_protocol_distribution()
        }

        report_path = os.path.join(self.output_dir, "pcap_forensic_report.json")
        with open(report_path, "w") as f:
            json.dump(report, f, indent=2)

        print(f"[*] Total packets: {report['total_packets']}")
        print(f"[*] Conversations: {len(report['conversations'])}")
        print(f"[*] DNS queries: {len(report['dns_queries'])}")
        print(f"[*] Potential beacons: {len(report['potential_beacons'])}")
        return report_path


def main():
    if len(sys.argv) < 3:
        print("Usage: python process.py <pcap_file> <output_dir>")
        sys.exit(1)
    analyzer = PCAPForensicAnalyzer(sys.argv[1], sys.argv[2])
    analyzer.generate_report()


if __name__ == "__main__":
    main()

References

• Wireshark Documentation: https://www.wireshark.org/docs/
• PCAP Analysis Mastery: https://insanecyber.com/mastering-pcap-review/
• SANS Network Forensics: https://www.sans.org/cyber-security-courses/network-forensics/
• Public PCAPs for Practice: https://www.netresec.com/?page=PcapFiles