Objective
Automatically discover and classify different types of attacker behavior (e.g., port scanning, login brute-forcing, web scanning) by applying unsupervised clustering to honeypot logs, providing actionable intelligence for security operations teams.
Business Value
For Security Operations Teams:- Threat Intelligence: Automatically categorize attacker behaviors without prior knowledge of attack signatures
- Pattern Recognition: Identify emerging attack patterns and group similar campaigns together
- Strategic Defense: Make data-driven decisions about security controls based on observed attacker clusters
- Alert Reduction: Focus on behavioral patterns rather than individual alerts to reduce noise
For Enterprise Security:- Proactive Defense: Understand attacker methodologies before they impact production systems
- Resource Allocation: Prioritize security investments based on most common attack patterns
- Incident Response: Faster triage through automatic behavioral classification
- Threat Hunting: Identify novel attack patterns that bypass signature-based detection
Core Libraries
- pandas & numpy: Data processing and aggregation of honeypot log entries
- scikit-learn: K-Means clustering for unsupervised behavioral pattern discovery
- matplotlib & seaborn: Cluster visualization and elbow method analysis
- sklearn.decomposition: PCA for dimensional reduction and cluster visualization
- sklearn.preprocessing: StandardScaler for feature normalization in clustering
Dataset
Source: Synthetically Generated Honeypot Logs- Attack Types: Port scanning, SSH brute-force, web application scanning
- Log Entries: Realistic honeypot connection attempts with source IPs, ports, protocols, and messages
- Behavioral Patterns: Distinct patterns per attack type (many ports vs single port, different error messages)
- Scale: Multiple source IPs with varying numbers of connection attempts per attacker
Key Behavioral Features:- Connection volume per source IP
- Port diversity (unique ports targeted)
- Protocol-specific failure patterns (SSH auth failures, HTTP 404s)
- Target port preferences and common attack patterns
Step-by-Step Guide
1. Environment Setup and Data Generation
pip install pandas numpy scikit-learn matplotlib seabornGenerate synthetic honeypot logs with realistic attack patterns for behavioral analysis.
2. Synthetic Honeypot Log Generation
# Define different attacker behavior patterns
behaviors = ['port_scan', 'brute_force_ssh', 'web_scan']
# Port scanners: many connections to different ports
if behavior == 'port_scan':
for _ in range(random.randint(80, 200)):
port = random.randint(1, 65535)
log_entries.append([ip, port, 'TCP', 'Connection refused'])
# SSH brute-forcers: many connections to port 22
elif behavior == 'brute_force_ssh':
for _ in range(random.randint(50, 150)):
log_entries.append([ip, 22, 'SSH', 'Authentication failed'])
3. Feature Engineering for Behavioral Profiling
# Aggregate raw logs by source IP to create behavioral profiles
attacker_profiles = df.groupby('source_ip').agg(
total_connections=('dest_port', 'count'),
unique_ports_targeted=('dest_port', 'nunique'),
ssh_auth_failures=('message', lambda x: x.str.contains('Authentication failed').sum()),
http_404_errors=('message', lambda x: x.str.contains('404').sum()),
common_port=('dest_port', lambda x: x.mode()[0])
)
4. Optimal Cluster Number Selection
# Use elbow method to determine optimal number of clusters
inertia = []
k_range = range(1, 10)
for k in k_range:
kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
kmeans.fit(X_scaled)
inertia.append(kmeans.inertia_)
5. K-Means Clustering for Behavioral Grouping
# Train K-Means with optimal number of clusters
optimal_k = 3
kmeans = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)
attacker_profiles['cluster'] = kmeans.fit_predict(X_scaled)
6. Cluster Analysis and Validation
# Analyze cluster centroids to understand behavioral patterns
cluster_analysis = attacker_profiles.groupby('cluster').mean(numeric_only=True)
# Cross-tabulate with ground truth for validation
pd.crosstab(attacker_profiles['cluster'], attacker_profiles['true_behavior'])
Success Criteria
Primary Metrics:- Cluster Separation: Clear distinction between behavioral patterns in cluster centroids
- Ground Truth Alignment: >80% agreement between clusters and known attack types
- Elbow Method Validation: Clear optimal k selection matching expected number of behaviors
Secondary Metrics:- Feature Interpretability: Logical cluster centroids reflecting attack behavior patterns
- Cluster Stability: Consistent results across multiple clustering runs
- Visualization Quality: Clear cluster separation in PCA-reduced dimensional space
Business Impact:- Deploy in SOC for automated threat categorization
- Integrate with SIEM systems for behavioral alerting
- Provide threat intelligence insights for defensive strategy
Next Steps & Extensions
Immediate Improvements
- Real Honeypot Integration: Connect with actual honeypot systems (Cowrie, DionaeaArtifact)
- Temporal Analysis: Incorporate time-based features for campaign tracking
- Hierarchical Clustering: Explore DBSCAN for density-based cluster discovery
Advanced Techniques
- Anomaly Detection: Identify completely novel attack patterns outside known clusters
- Ensemble Clustering: Combine multiple clustering algorithms for robust results
- Sequential Analysis: Track attacker behavior evolution over time
Production Deployment
- Real-time Processing: Stream processing pipeline for live honeypot data
- Alert Integration: Automatic SIEM rule generation based on discovered clusters
- Dashboard Development: Interactive visualization for security analysts
Domain Expansion
- Multi-source Integration: Combine honeypot data with network traffic and endpoint logs
- Attribution Analysis: Cluster attackers by infrastructure and tooling patterns
- Campaign Tracking: Link individual attacks to broader threat actor campaigns
This project demonstrates practical application of unsupervised learning for cybersecurity intelligence, providing both automated threat categorization and strategic insights for security operations teams.