performing-network-traffic-analysis-with-zeek▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Deploy Zeek network security monitor to capture, parse, and analyze network traffic metadata for threat detection, anomaly identification, and forensic investigation.
| name | performing-network-traffic-analysis-with-zeek |
| description | Deploy Zeek network security monitor to capture, parse, and analyze network traffic metadata for threat detection, anomaly identification, and forensic investigation. |
| domain | cybersecurity |
| subdomain | network-security |
| tags | - zeek - network-monitoring - traffic-analysis - ids - nids - pcap - threat-detection - forensics - siem-integration |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - PR.IR-01 - DE.CM-01 - ID.AM-03 - PR.DS-02 |
Performing Network Traffic Analysis with Zeek
Overview
Zeek (formerly Bro) is an open-source network analysis framework that operates as a passive network security monitor. Unlike traditional signature-based IDS tools, Zeek generates high-fidelity structured logs from observed network traffic, capturing detailed metadata for protocols including HTTP, DNS, TLS, SSH, SMTP, FTP, and dozens more. Zeek's extensible scripting language enables custom detection logic, behavioral analysis, and automated response. This skill covers deploying Zeek, understanding its log architecture, writing custom detection scripts, and integrating outputs with SIEM platforms.
When to Use
- When conducting security assessments that involve performing network traffic analysis with zeek
- 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
Prerequisites
- Linux server (Ubuntu 22.04+ or CentOS 8+) with 4+ CPU cores and 8GB+ RAM
- Network TAP or SPAN port mirroring configured for traffic capture
- Zeek 6.0+ installed (via package manager or source compilation)
- Root or capture group privileges for packet capture
- SIEM platform (Splunk, ELK Stack, or QRadar) for log ingestion
Core Concepts
Zeek Architecture
Zeek operates in two main modes:
- Live Capture - Monitors traffic in real-time on one or more network interfaces
- Offline Analysis - Processes saved PCAP files for retrospective analysis
The processing pipeline consists of:
- Packet Capture Layer - Reads raw packets from interfaces or PCAP files
- Event Engine - Reassembles TCP streams and generates protocol events
- Script Interpreter - Executes Zeek scripts that process events and generate logs
- Log Framework - Writes structured logs in TSV, JSON, or custom formats
Log Architecture
Zeek generates protocol-specific log files:
| Log File | Description |
|---|---|
conn.log | TCP/UDP/ICMP connection summaries with duration, bytes, state |
dns.log | DNS queries and responses with query type, answers, TTL |
http.log | HTTP requests/responses with URIs, user agents, MIME types |
ssl.log | TLS handshake details including certificate chain, JA3/JA3S |
files.log | File transfers with MIME types, hashes (MD5, SHA1, SHA256) |
notice.log | Alerts generated by Zeek detection scripts |
weird.log | Protocol anomalies and unexpected behaviors |
x509.log | Certificate details from TLS connections |
smtp.log | Email metadata including sender, recipient, subject |
ssh.log | SSH connection details and authentication results |
pe.log | Portable Executable file metadata |
dpd.log | Dynamic Protocol Detection failures |
Workflow
Step 1: Install and Configure Zeek
# Install Zeek on Ubuntu
sudo apt-get install -y zeek
# Or install from Zeek repository
echo 'deb http://download.opensuse.org/repositories/security:/zeek/xUbuntu_22.04/ /' | \
sudo tee /etc/apt/sources.list.d/zeek.list
sudo apt-get update && sudo apt-get install -y zeek-lts
# Verify installation
zeek --version
Configure the node layout in /opt/zeek/etc/node.cfg:
[manager]
type=manager
host=localhost
[proxy-1]
type=proxy
host=localhost
[worker-1]
type=worker
host=localhost
interface=eth0
lb_method=pf_ring
lb_procs=4
[worker-2]
type=worker
host=localhost
interface=eth1
lb_method=pf_ring
lb_procs=4
Configure network definitions in /opt/zeek/etc/networks.cfg:
# Internal network ranges
10.0.0.0/8 Private RFC1918
172.16.0.0/12 Private RFC1918
192.168.0.0/16 Private RFC1918
Step 2: Configure Logging and Output
Edit /opt/zeek/share/zeek/site/local.zeek:
# Load standard detection scripts
@load base/protocols/conn
@load base/protocols/dns
@load base/protocols/http
@load base/protocols/ssl
@load base/protocols/ssh
@load base/protocols/smtp
@load base/protocols/ftp
# Load file analysis
@load base/files/hash-all-files
@load base/files/extract-all-files
# Load detection frameworks
@load base/frameworks/notice
@load base/frameworks/intel
@load base/frameworks/files
@load base/frameworks/software
# Load additional protocol analyzers
@load policy/protocols/ssl/validate-certs
@load policy/protocols/ssl/log-hostcerts-only
@load policy/protocols/ssh/detect-bruteforcing
@load policy/protocols/dns/detect-external-names
@load policy/protocols/http/detect-sqli
# Enable JA3 fingerprinting
@load policy/protocols/ssl/ja3
# Enable JSON output for SIEM ingestion
@load policy/tuning/json-logs
redef LogAscii::use_json = T;
# Configure file extraction directory
redef FileExtract::prefix = "/opt/zeek/extracted/";
# Set notice email
redef Notice::mail_dest = "[email protected]";
Step 3: Write Custom Detection Scripts
Create detection scripts for common threats:
Detect DNS Tunneling (/opt/zeek/share/zeek/site/detect-dns-tunnel.zeek):
@load base/protocols/dns
module DNSTunnel;
export {
redef enum Notice::Type += {
DNS_Tunnel_Suspected
};
# Threshold for suspicious DNS query length
const query_len_threshold = 50 &redef;
# Track query counts per host per domain
global dns_query_counts: table[addr, string] of count &default=0 &create_expire=5min;
# High query volume threshold
const query_volume_threshold = 100 &redef;
}
event dns_request(c: connection, msg: dns_msg, query: string, qtype: count, qclass: count)
{
if ( |query| > query_len_threshold )
{
local parts = split_string(query, /\./);
if ( |parts| > 3 )
{
local base_domain = cat(parts[|parts|-2], ".", parts[|parts|-1]);
dns_query_counts[c$id$orig_h, base_domain] += 1;
if ( dns_query_counts[c$id$orig_h, base_domain] > query_volume_threshold )
{
NOTICE([$note=DNS_Tunnel_Suspected,
$msg=fmt("Possible DNS tunneling: %s queries to %s with long query names",
c$id$orig_h, base_domain),
$conn=c,
$identifier=cat(c$id$orig_h, base_domain),
$suppress_for=30min]);
}
}
}
}
Detect Beaconing Behavior (/opt/zeek/share/zeek/site/detect-beaconing.zeek):
@load base/protocols/conn
module Beaconing;
export {
redef enum Notice::Type += {
C2_Beacon_Detected
};
# Track connection intervals
global conn_intervals: table[addr, addr, port] of vector of time &create_expire=1hr;
const min_connections = 20 &redef;
const jitter_threshold = 0.15 &redef;
}
event connection_state_remove(c: connection)
{
if ( c$id$resp_p == 80/tcp || c$id$resp_p == 443/tcp )
{
local key = [c$id$orig_h, c$id$resp_h, c$id$resp_p];
if ( key !in conn_intervals )
conn_intervals[key] = vector();
conn_intervals[key] += network_time();
if ( |conn_intervals[key]| >= min_connections )
{
local intervals: vector of interval = vector();
local i = 1;
while ( i < |conn_intervals[key]| )
{
intervals += conn_intervals[key][i] - conn_intervals[key][i-1];
i += 1;
}
# Calculate mean and standard deviation
local sum_val = 0.0;
for ( idx in intervals )
sum_val += interval_to_double(intervals[idx]);
local mean_val = sum_val / |intervals|;
local variance = 0.0;
for ( idx in intervals )
{
local diff = interval_to_double(intervals[idx]) - mean_val;
variance += diff * diff;
}
variance = variance / |intervals|;
local stddev = sqrt(variance);
if ( mean_val > 0 && (stddev / mean_val) < jitter_threshold )
{
NOTICE([$note=C2_Beacon_Detected,
$msg=fmt("Possible C2 beaconing: %s -> %s:%s (interval=%.1fs, jitter=%.2f)",
c$id$orig_h, c$id$resp_h, c$id$resp_p,
mean_val, stddev/mean_val),
$conn=c,
$identifier=cat(c$id$orig_h, c$id$resp_h),
$suppress_for=1hr]);
}
}
}
}
Step 4: Configure Intel Framework
Load threat intelligence feeds into Zeek:
# In local.zeek
@load frameworks/intel/seen
@load frameworks/intel/do_notice
redef Intel::read_files += {
"/opt/zeek/intel/malicious-ips.intel",
"/opt/zeek/intel/malicious-domains.intel",
"/opt/zeek/intel/malicious-hashes.intel",
};
Intel file format (/opt/zeek/intel/malicious-ips.intel):
#fields indicator indicator_type meta.source meta.desc meta.do_notice
198.51.100.50 Intel::ADDR abuse.ch Known C2 server T
203.0.113.100 Intel::ADDR threatfeed Ransomware infrastructure T
Step 5: Deploy and Operate
# Deploy Zeek cluster
sudo /opt/zeek/bin/zeekctl deploy
# Check cluster status
sudo /opt/zeek/bin/zeekctl status
# Process offline PCAP
zeek -r capture.pcap local.zeek
# View logs
cat /opt/zeek/logs/current/conn.log | zeek-cut id.orig_h id.resp_h id.resp_p proto service duration orig_bytes resp_bytes
# Search for specific connections
cat /opt/zeek/logs/current/dns.log | zeek-cut query answers | grep -i "suspicious"
# Rotate logs
sudo /opt/zeek/bin/zeekctl cron
Step 6: SIEM Integration
Filebeat configuration for ELK Stack:
filebeat.inputs:
- type: log
enabled: true
paths:
- /opt/zeek/logs/current/*.log
json.keys_under_root: true
json.add_error_key: true
fields:
source: zeek
fields_under_root: true
output.elasticsearch:
hosts: ["https://elasticsearch:9200"]
index: "zeek-%{+yyyy.MM.dd}"
setup.template.name: "zeek"
setup.template.pattern: "zeek-*"
Analysis Techniques
Connection Analysis
# Find top talkers by bytes
cat conn.log | zeek-cut id.orig_h orig_bytes | sort -t$'\t' -k2 -rn | head -20
# Find long-duration connections (potential C2)
cat conn.log | zeek-cut id.orig_h id.resp_h id.resp_p duration | awk '$4 > 3600' | sort -t$'\t' -k4 -rn
# Find connections with unusual ports
cat conn.log | zeek-cut id.resp_p proto | sort | uniq -c | sort -rn | head -30
TLS Analysis
# Find self-signed certificates
cat ssl.log | zeek-cut server_name validation_status | grep "self signed"
# Extract JA3 fingerprints for known malware
cat ssl.log | zeek-cut ja3 server_name | sort | uniq -c | sort -rn
# Find expired certificates
cat ssl.log | zeek-cut server_name not_valid_after | awk -F'\t' '$2 < systime()'
Best Practices
- TAP Over SPAN - Use network TAPs instead of SPAN ports to avoid packet loss under load
- Worker Scaling - Assign 1 Zeek worker per 1 Gbps of monitored traffic
- AF_PACKET Clusters - Use AF_PACKET with load balancing for multi-core processing
- Log Rotation - Configure automatic log rotation and archival (default: hourly)
- Intel Updates - Automate threat intelligence feed updates at least daily
- Packet Loss Monitoring - Monitor
capture_loss.logfor dropped packets - Custom Scripts - Develop organization-specific detections based on threat landscape
References
How to use performing-network-traffic-analysis-with-zeek on Cursor
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Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add performing-network-traffic-analysis-with-zeek
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-network-traffic-analysis-with-zeek from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate performing-network-traffic-analysis-with-zeek. Access the skill through slash commands (e.g., /performing-network-traffic-analysis-with-zeek) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
Exploratory Data Analysis
Quickly understand datasets, identify patterns, and generate insights
Example
Analyze CSV with 100K rows, identify outliers, visualize correlations, suggest hypotheses
Reduce EDA time from hours to minutes, uncover insights faster
Data Cleaning & Transformation
Write scripts to clean messy data, handle missing values, normalize formats
Example
Generate Python/SQL to fix date formats, impute missing values, remove duplicates
Automate 80% of data preprocessing work
Statistical Analysis
Perform hypothesis testing, regression, and statistical modeling
Example
Run A/B test analysis, calculate confidence intervals, interpret p-values
Get statistically sound analysis without PhD in statistics
Data Visualization
Create charts, dashboards, and visual reports
Example
Generate matplotlib/seaborn code for time series plots, distribution charts, heatmaps
Build presentation-ready visualizations 3x faster
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Python environment (pandas, numpy, matplotlib) or SQL database access
- ›Basic understanding of data analysis concepts
- ›Sample datasets for testing skill capabilities
Time Estimate
20-40 minutes to set up and run first analysis
Installation Steps
- 1.Install data analysis skill using provided command
- 2.Prepare a sample dataset (CSV, JSON, or database connection)
- 3.Start with descriptive statistics: 'Summarize this dataset'
- 4.Progress to visualization: 'Create a scatter plot of X vs Y'
- 5.Advanced analysis: 'Run linear regression and interpret results'
- 6.Validate outputs: check calculations, verify visualizations make sense
- 7.Document analysis workflow for reproducibility
Common Pitfalls
- ⚠Not validating statistical assumptions before applying tests
- ⚠Accepting visualizations without checking data accuracy
- ⚠Overlooking data quality issues (missing values, outliers)
- ⚠Misinterpreting correlation as causation
- ⚠Using wrong statistical test for data distribution
- ⚠Not considering sample size and statistical power
Best Practices▌
✓ Do
- +Always validate data quality before analysis
- +Check statistical assumptions (normality, independence, etc.)
- +Visualize data before running statistical tests
- +Document analysis steps for reproducibility
- +Cross-validate findings with domain experts
- +Use skill for initial exploration, then dive deeper manually
- +Save generated code for reuse on similar datasets
✗ Don't
- −Don't trust analysis without verifying data quality
- −Don't apply statistical tests without checking assumptions
- −Don't make business decisions solely on AI-generated analysis
- −Don't ignore outliers without investigating cause
- −Don't skip data validation and sanity checks
- −Don't use for mission-critical financial or medical analysis without expert review
💡 Pro Tips
- ★Describe data context: 'This is user behavior data from e-commerce site'
- ★Ask for interpretation: 'What does this correlation mean for business?'
- ★Request multiple approaches: 'Show 3 ways to handle missing data'
- ★Combine AI analysis with domain expertise for best insights
- ★Use for rapid prototyping, then refine analysis manually
When to Use This▌
✓ Use When
Use for exploratory data analysis, data cleaning, statistical testing, visualization prototyping, and learning new analysis techniques. Best for initial exploration and rapid insights.
✗ Avoid When
Avoid for mission-critical financial analysis, medical research requiring regulatory compliance, production ML models, or when deep statistical expertise is required for nuanced interpretation.
Learning Path▌
- 1Basic: descriptive statistics, data cleaning, simple visualizations
- 2Intermediate: hypothesis testing, regression, correlation analysis
- 3Advanced: time series analysis, clustering, predictive modeling
- 4Expert: causal inference, experimental design, advanced statistical methods
Discussion
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Ratings
4.7★★★★★28 reviews- ★★★★★Shikha Mishra· Dec 8, 2024
I recommend performing-network-traffic-analysis-with-zeek for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Yash Thakker· Nov 27, 2024
performing-network-traffic-analysis-with-zeek fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakshi Patil· Nov 19, 2024
Useful defaults in performing-network-traffic-analysis-with-zeek — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Oct 18, 2024
performing-network-traffic-analysis-with-zeek has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Chaitanya Patil· Oct 10, 2024
Registry listing for performing-network-traffic-analysis-with-zeek matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Oshnikdeep· Sep 25, 2024
Keeps context tight: performing-network-traffic-analysis-with-zeek is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Amina Park· Sep 25, 2024
performing-network-traffic-analysis-with-zeek fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Aanya Rahman· Sep 13, 2024
performing-network-traffic-analysis-with-zeek has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Layla Choi· Sep 9, 2024
Registry listing for performing-network-traffic-analysis-with-zeek matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Aanya Khan· Aug 28, 2024
Useful defaults in performing-network-traffic-analysis-with-zeek — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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