Introduction
Understanding how attackers operate is critical to building stronger defenses in cybersecurity. Honeypots are powerful tools for studying adversary tactics in a controlled environment, capturing threat intelligence, and gaining insights into attacker behavior. To sharpen my practical skills, I deployed a high-interaction honeypot in AWS to monitor unauthorized activity and analyze emerging threats. In this blog, I’ll share my objectives, implementation process, challenges, findings, and key lessons learned.
Project Objective
The goal was to design and deploy a honeypot in AWS that mimics a vulnerable system to attract attackers. The setup aimed to:
- Gather detailed threat intelligence.
- Analyze attacker methodologies and techniques.
- Deepen my understanding of threat detection and cloud security practices.
Tools & Technologies
- AWS EC2: Scalable and secure compute resources.
- Cowrie: A high-interaction SSH honeypot for capturing detailed attacker activities.
- AWS CloudWatch: For real-time monitoring and logging.
- AWS S3: For secure, scalable log storage.
- Python: For automating data analysis and reporting.
How I Built It
Environment Setup
- EC2 Instance Deployment: Launched an EC2 instance configured to look like a valuable target using a common operating system AMI. Applied strict security group rules to control traffic, allowing only honeypot-specific interactions. Used Network ACLs to add an extra security layer.
Honeypot Deployment
- Installing Cowrie: Set up Cowrie to emulate a vulnerable SSH server. Configured it to log attacker activities, including login attempts, commands, and file uploads/downloads.
- Database Integration: Integrated a local SQLite database with Cowrie for efficient storage and easy retrieval of data.
Logging and Monitoring
- CloudWatch Integration: Configured AWS CloudWatch to collect and monitor honeypot logs in real-time. Set up CloudWatch Alarms to notify me of suspicious activities.
- AWS S3 Storage: Stored historical logs in AWS S3 for scalability and long-term analysis.
Data Analysis
- Threat Pattern Identification: Used Python scripts to extract and visualize key threat indicators, such as common attack vectors and frequently used brute-force credentials.
- Cross-Referencing: Validated and contextualized attacker behavior using known threat intelligence feeds.
Challenges Faced
- Stealth Configuration: Balancing visibility to attract attackers while avoiding detection. Used advanced obfuscation techniques to make the honeypot blend in with legitimate systems.
- Cost Management: Selected cost-efficient EC2 instance types and optimized resource usage to keep costs manageable.
- Data Volume: Configured AWS S3 for scalable storage to handle large datasets generated by the honeypot.
Key Findings
- Attack Sources: Captured over 30 unique IP addresses, many originating from botnets and automated scanning tools.
- Credential Analysis: Observed repeated use of weak default credentials, emphasizing the ongoing threat of credential stuffing.
- Attack Techniques: Documented malware deployment scripts and lateral movement attempts, providing insights into advanced persistent threats (APTs).
- Threat Intelligence Contribution: Shared anonymized data with security communities to enhance collective defenses.
Lessons Learned
This project provided valuable insights, including:
- Proactive Monitoring Matters: Honeypots deliver actionable intelligence that strengthens overall defense strategies.
- AWS Security Practices: Deepened my knowledge of AWS services like EC2, IAM roles, and CloudWatch for robust security monitoring.
- Incident Response Preparation: Simulating real-world attack scenarios improved my ability to detect and mitigate security incidents.
- Data Analysis Skills: Enhanced my data processing techniques by analyzing large datasets and automating insights.
Conclusion
Deploying a high-interaction honeypot in AWS was a valuable experience that enhanced my understanding of attacker behavior and cloud security practices. Capturing and analyzing real-world attack patterns provided critical insights, improving my incident response capabilities and preparing me for roles that require strong skills in threat detection and analysis. This project reinforced the importance of proactive threat intelligence in building a more secure digital environment.