analyzing-network-traffic-for-incidents

Q: How do I install analyzing-network-traffic-for-incidents?

Run npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-network-traffic-for-incidents in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/analyzing-network-traffic-for-incidents/ (personal) or .claude/skills/analyzing-network-traffic-for-incidents/ (project).

by mukul975AI & Agents

Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration detection.

Installs: 432
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-network-traffic-for-incidents

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads analyzing-network-traffic-for-incidents automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Analyzing Network Traffic for Incidents

When to Use

SIEM alerts on anomalous network traffic patterns requiring deeper investigation
C2 beaconing is suspected and needs confirmation through packet-level analysis
Data exfiltration volume or destination must be quantified from network evidence
Lateral movement between systems needs to be traced through network connections
An IDS/IPS alert requires packet-level validation to confirm or dismiss

Do not use for host-based forensic analysis (process execution, file system artifacts); use endpoint forensics tools instead.

Prerequisites

Full packet capture (PCAP) infrastructure or on-demand capture capability (network tap, SPAN port)
Wireshark installed on the analysis workstation with appropriate display filters knowledge
Zeek (formerly Bro) deployed for network metadata generation (conn.log, dns.log, http.log, ssl.log)
NetFlow/IPFIX collection from network devices for traffic flow analysis
Network architecture diagram showing VLAN layout, firewall placement, and monitoring points
Threat intelligence feeds for correlating observed network indicators

Workflow

Step 1: Capture or Acquire Network Traffic

Obtain the relevant traffic data for the investigation:

Live Capture (if incident is active):

# Capture on specific interface filtering by host
tcpdump -i eth0 -w capture.pcap host 10.1.5.42

# Capture C2 traffic to specific external IP
tcpdump -i eth0 -w c2_traffic.pcap host 185.220.101.42

# Capture with rotation (1GB files, keep 10)
tcpdump -i eth0 -w capture_%Y%m%d%H%M.pcap -C 1000 -W 10

From Existing Infrastructure:

Export PCAP from full packet capture appliance (Arkime/Moloch, ExtraHop, Corelight)
Pull Zeek logs from the Zeek cluster for the investigation timeframe
Export NetFlow data from network devices for high-level traffic analysis

Step 2: Identify C2 Communications

Detect command-and-control traffic patterns:

Beaconing Detection (Zeek conn.log):

# Extract connections to external IPs with regular intervals
cat conn.log | zeek-cut ts id.orig_h id.resp_h id.resp_p duration orig_bytes resp_bytes \
  | awk '$4 ~ /^185\.220/' | sort -t. -k1,1n -k2,2n

Wireshark Beacon Analysis:

# Filter for traffic to suspected C2 IP
ip.addr == 185.220.101.42

# Filter HTTPS traffic to non-standard ports
tcp.port != 443 && ssl

# Filter DNS queries for suspicious domains
dns.qry.name contains "evil" or dns.qry.name matches "^[a-z0-9]{32}\."

# Filter HTTP POST (common C2 check-in method)
http.request.method == "POST" && ip.dst == 185.220.101.42

Beaconing characteristics to identify:

Regular time intervals between connections (e.g., every 60 seconds with 10-15% jitter)
Consistent packet sizes in requests and responses
HTTPS to external IPs not associated with legitimate CDNs or services
DNS queries with high entropy subdomains (DNS tunneling indicator)

Step 3: Analyze Lateral Movement Traffic

Trace adversary movement between internal systems:

Key protocols for lateral movement detection:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SMB (TCP 445):     PsExec, file share access, ransomware propagation
RDP (TCP 3389):    Remote desktop sessions
WinRM (TCP 5985):  PowerShell remoting
WMI (TCP 135):     Remote command execution
SSH (TCP 22):      Linux lateral movement
DCE/RPC (TCP 135): DCOM-based lateral movement

Wireshark Filters for Lateral Movement:

# SMB lateral movement
smb2 && ip.src == 10.1.5.42 && ip.dst != 10.1.5.42

# RDP connections from compromised host
tcp.dstport == 3389 && ip.src == 10.1.5.42

# Kerberos ticket requests (potential pass-the-ticket)
kerberos.msg_type == 12 && ip.src == 10.1.5.42

# NTLM authentication (potential pass-the-hash)
ntlmssp.auth.username && ip.src == 10.1.5.42

Step 4: Detect Data Exfiltration

Identify unauthorized data transfers leaving the network:

# Identify large outbound transfers in Zeek conn.log
cat conn.log | zeek-cut ts id.orig_h id.resp_h id.resp_p orig_bytes \
  | awk '$5 > 100000000' | sort -t$'\t' -k5 -rn

# DNS tunneling detection (high volume of TXT queries)
cat dns.log | zeek-cut query qtype | grep TXT | cut -f1 \
  | rev | cut -d. -f1,2 | rev | sort | uniq -c | sort -rn | head

# Unusual protocol usage (ICMP tunneling, DNS over HTTPS)
cat conn.log | zeek-cut proto id.resp_p orig_bytes | awk '$1 == "icmp" && $3 > 1000'

Wireshark Exfiltration Filters:

# Large HTTP POST uploads
http.request.method == "POST" && tcp.len > 10000

# FTP data transfers
ftp-data && ip.src == 10.0.0.0/8

# DNS with large TXT responses (tunneling)
dns.resp.type == 16 && dns.resp.len > 200

Step 5: Extract and Correlate IOCs

Pull network-based indicators from traffic analysis:

External IP addresses contacted by compromised hosts
Domains resolved via DNS during the incident timeframe
URLs accessed via HTTP/HTTPS (if SSL inspection is in place)
TLS certificate details (subject, issuer, serial number, JA3/JA3S hashes)
User-Agent strings from HTTP requests
File transfers captured in PCAP (extract using Wireshark Export Objects)

Step 6: Document Network Forensic Findings

Compile analysis into a structured report with evidence references:

Reference specific PCAP files, frame numbers, and timestamps for each finding
Include packet captures of key evidence as screenshots or exported PDFs
Map network activity to the incident timeline
Correlate network findings with host-based evidence from endpoint forensics

Key Concepts

Term	Definition
PCAP (Packet Capture)	File format storing raw network packets captured from a network interface for offline analysis
Beaconing	Regular, periodic network connections from a compromised host to a C2 server, identifiable by consistent timing intervals
JA3/JA3S	TLS client and server fingerprinting method based on the ClientHello and ServerHello parameters; unique per application
NetFlow/IPFIX	Network traffic metadata (source, destination, ports, bytes, duration) collected by routers and switches without full packet capture
DNS Tunneling	Technique encoding data in DNS queries and responses to exfiltrate data or maintain C2 through DNS protocol
Network Tap	Hardware device that creates an exact copy of network traffic for monitoring without impacting network performance
Zeek Logs	Structured metadata logs generated by the Zeek network analysis framework covering connections, DNS, HTTP, SSL, and more

Tools & Systems

Wireshark: Open-source packet analyzer for deep inspection of network protocols at the packet level
Zeek (formerly Bro): Network analysis framework generating structured metadata logs from live or captured traffic
Arkime (formerly Moloch): Open-source full packet capture and search platform for large-scale network forensics
NetworkMiner: Network forensic analysis tool for extracting files, images, and credentials from PCAP files
RITA (Real Intelligence Threat Analytics): Open-source beacon detection and DNS tunneling analysis tool for Zeek logs

Common Scenarios

Scenario: Confirming C2 Beaconing and Quantifying Exfiltration

Context: EDR detects a suspicious process on a workstation but cannot determine the volume of data exfiltrated. Network team provides PCAP from the full packet capture appliance covering the incident timeframe.

Approach:

Filter PCAP to traffic from the compromised host IP to external destinations
Identify the C2 channel by analyzing connection timing patterns (beacon detection)
Extract TLS certificate and JA3 hash from the C2 connection for IOC generation
Calculate total bytes transferred to C2 infrastructure over the incident duration
Check for additional exfiltration channels (DNS tunneling, cloud storage uploads)
Extract any unencrypted files transferred using Wireshark Export Objects feature

Pitfalls:

Analyzing only HTTP traffic when C2 is operating over HTTPS without SSL inspection
Missing DNS tunneling because the data volume per query is small (but total over time is significant)
Not correlating network timestamps with endpoint timestamps (timezone mismatches)
Overlooking legitimate cloud services abused for exfiltration (OneDrive, Google Drive, Dropbox)

Output Format

NETWORK TRAFFIC ANALYSIS REPORT
=================================
Incident:         INC-2025-1547
Analyst:          [Name]
Capture Source:   Arkime full packet capture
Analysis Period:  2025-11-15 14:00 UTC - 2025-11-15 18:00 UTC
Total PCAP Size:  4.7 GB

C2 COMMUNICATIONS
Source:           10.1.5.42 (WKSTN-042)
Destination:      185.220.101.42:443 (HTTPS)
Beacon Interval:  60 seconds ± 12% jitter
Sessions:         237 connections over 4 hours
JA3 Hash:         a0e9f5d64349fb13191bc781f81f42e1
TLS Certificate:  CN=update.evil[.]com (self-signed)
Total Data Sent:  147 MB (outbound)
Total Data Recv:  2.3 MB (inbound - commands)

LATERAL MOVEMENT
10.1.5.42 → 10.1.10.15 (SMB, TCP 445) - 14:35 UTC
10.1.5.42 → 10.1.10.20 (RDP, TCP 3389) - 14:42 UTC
10.1.5.42 → 10.1.1.5  (LDAP, TCP 389) - 15:10 UTC

EXFILTRATION SUMMARY
Protocol:         HTTPS to C2 server
Volume:           147 MB outbound
Duration:         14:23 UTC - 18:00 UTC
Files Extracted:  [list if recoverable from unencrypted channels]

DNS ANALYSIS
Suspicious Queries: 0 DNS tunneling indicators
DGA Detection:      0 algorithmically generated domains

EVIDENCE REFERENCES
PCAP File:        INC-2025-1547_capture.pcap (SHA-256: ...)
Zeek Logs:        /logs/zeek/2025-11-15/ (conn.log, ssl.log, dns.log)

FAQ

What does the analyzing-network-traffic-for-incidents skill do?

How do I install analyzing-network-traffic-for-incidents?

Run npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-network-traffic-for-incidents in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/analyzing-network-traffic-for-incidents/ (personal) or .claude/skills/analyzing-network-traffic-for-incidents/ (project).

Which AI agents can use analyzing-network-traffic-for-incidents?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is analyzing-network-traffic-for-incidents safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains analyzing-network-traffic-for-incidents?

It is published by mukul975 in mukul975/anthropic-cybersecurity-skills. It has 432 recorded installs.