Project Case Study: Live Honeypot Deployment

Project Overview

This project involved deploying a production-grade DShield honeypot to capture and analyze live attack data from the internet. The system was designed to collect threat intelligence, identify attack patterns, and provide real-time insights into malicious activities targeting exposed systems.

Technical Architecture

• Honeypot: DShield Honeypot (Internet Storm Center)
• Containerization: Docker for isolated deployment
• Cloud Infrastructure: AWS EC2 with dedicated security groups
• Data Pipeline: CloudWatch Logs → Lambda → DynamoDB
• Visualization: CloudWatch Dashboards and Grafana
• Alerting: SNS notifications and Discord webhooks

Implementation Details

1. Honeypot Deployment

The DShield honeypot was deployed using Docker containers to ensure isolation and easy management. The honeypot simulates vulnerable services including SSH, Telnet, and web services to attract attackers.

Docker Configuration:

docker run -d --name dshield-honeypot \
  -p 22:2222 \
  -p 23:2223 \
  -p 80:8080 \
  -p 443:8443 \
  -v /var/log/dshield:/var/log/dshield \
  dshield/honeypot:latest
                            

2. Data Collection & Processing

A sophisticated data pipeline was implemented to process honeypot logs in real-time:

• Log Aggregation: CloudWatch agent collects logs from the honeypot container
• Stream Processing: Lambda functions parse and enrich log data
• Data Storage: DynamoDB for structured threat data storage
• Real-time Processing: Kinesis Data Streams for live data flow

3. Threat Intelligence Extraction

The system automatically extracts and categorizes threat intelligence including:

• IP Reputation: Attacker IP addresses with geolocation data
• Attack Patterns: Common usernames, passwords, and payloads
• Malware Samples: Captured payloads for further analysis
• IOCs: Indicators of Compromise for threat hunting

Security Considerations

Isolation & Containment

• Network Segmentation: Dedicated VPC with restricted outbound access
• Container Security: Read-only file systems and resource limits
• Monitoring: Continuous monitoring for container breakout attempts
• Incident Response: Automated shutdown procedures for security incidents

Data Protection

• Anonymization: PII scrubbing before data storage
• Encryption: All data encrypted in transit and at rest
• Access Control: IAM roles with least-privilege access
• Audit Logging: Complete audit trail of all system access

Results & Intelligence Gathered

Attack Statistics (30-Day Period)

Key Findings

• Automated Scanning: 89% of attempts were from automated tools/botnets
• Credential Stuffing: Top 10 passwords accounted for 76% of attempts
• Geographic Trends: Attacks correlated with known botnet command centers
• Timing Patterns: Clear circadian rhythm in attack volumes

Live Dashboard Features

• Real-time Attack Map: Geographic visualization of incoming attacks
• Service Metrics: Attack distribution across different services
• Threat Feed: Live stream of attack attempts with IOC extraction
• Historical Trends: Time-series analysis of attack patterns
• Alert System: Notifications for high-priority threats

Lessons Learned

This project provided valuable insights into real-world attack methodologies and the importance of proactive threat intelligence gathering. The honeypot demonstrated that even seemingly "empty" systems are constantly under attack, highlighting the need for robust security measures on all internet-facing systems.

The automated data processing pipeline proved crucial for handling the high volume of attack data, and the real-time alerting system enabled rapid response to emerging threats.

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