ClusterHawk
Threat Infrastructure Profiling,
Tracking & Early Warning
Profile and track threat infrastructure that reputation feeds miss. Submit IP datasets and get deep actor-network analysis, predictive models that flag emerging infrastructure before it goes active, automated intelligence reports, pattern discovery, and noise intelligence mining — all in one platform. Expand small sets of validated indicators into full infrastructure maps, profiling entire operator networks from limited seed data.
See the demo or request access
Learn more about ClusterHawkWhy ClusterHawk
Proactive Threat Detection
Identify potentially malicious IPs before they become active threats using predictive models trained on your specific attack patterns and infrastructure behaviors.
Academically Validated Methodology
Built on peer-reviewed research with proprietary evaluation methods, enabling detection of infrastructure patterns specialists would miss and threats before they appear in traditional tools.
Customizable Workflows
Configure clustering options, apply custom labeling rules, and create specialized models for your specific use cases with adaptive configuration frameworks and neural network architectures.
Intelligence Worth the Investment
Deep analysis takes hours but reveals intelligence worth waiting for. GPU-accelerated processing maintains analytical depth while delivering insights that would take analyst teams weeks to compile manually.
Complete Operational Intelligence
Analysis provides actionable intelligence for different stakeholders: organizational profiling, differentiating features for defenders, vulnerability analysis for risk teams, and malware indicators for threat hunters.
Transparent Decisions
SHAP and LIME-based explainability eliminates black box concerns with auditable cluster explanations, instance-level reasoning, feature importance rankings, and detailed analysis that meets regulatory compliance requirements.
Key Features
Infrastructure Profiling
Surfaces threat infrastructure invisible to reputation feeds — datasets from 100 to 5,000+ addresses, profiled through proprietary weighted ensemble analysis.
Pattern Recognition & Labeling
Custom labeling rules identify malicious clusters, extending threat detection to previously unknown IP addresses. Automatic cluster labeling based on rule context (e.g., SSH bruteforce → Lazarus Group) helps prioritize response efforts.
Model Training & Prediction
Possibility of training custom models based on your specific attack patterns to automatically identify new threats matching previous attack patterns. Each IP receives confidence scores (e.g., 63% confidence for APT29 phishing, 73% for Lazarus Group) to prioritize investigation.
Neighborhood Analysis
Track IP movement between clusters across different jobs, identifying IPs that frequently change behavior or association. Analysis includes stability scores, comparison job distribution, and detailed neighbor changes for each IP address.
Deep Analysis Processing
Deep analysis takes hours but delivers insights that would take analyst teams weeks to compile manually. Submit jobs before leaving the office for detailed infrastructure insights the next morning.
Pattern Discovery
Multi-dimensional similarity analysis with hierarchical clustering validation metrics. Infrastructure fingerprinting examines service stacks, TLS configurations, certificate patterns, and vulnerability landscapes. Compound queries across 15+ parameters produce unique operator signatures — catching patterns invisible to manual analysis.
Automated Threat Intelligence Reports
One-click conversion of clustering results into Markdown threat reports with SHAP/LIME-ranked feature analysis, competing hypotheses, and operational recommendations for immediate deployment.
Noise Intelligence Mining
Adaptive re-clustering of noise clusters reveals sophisticated adversary infrastructure that conventional tools discard, uncovering emerging threats through similarity analysis and anomaly detection.
Statistical Intelligence
Organizational profiling with geographic distribution analysis, vulnerability intelligence with CVE/EPSS integration, malware indicator profiling, and differentiating feature analytics for complete infrastructure understanding.
SHAP + LIME explainability
Transparent cluster explanations with feature importance rankings, confidence scores, and detailed reasoning behind clustering decisions, enabling analysts to understand and validate results.
Structural Anomaly Detection (SAD)
Anomaly detection identifies threats without training data using topological analysis and mathematical concepts, uncovering sophisticated attacks designed to evade traditional detection methods.
Read Our Research
See how ClusterHawk profiles real-world threat infrastructure — from nation-state campaigns to compromised router networks.
Threat Intelligence PublicationsHow Teams Use ClusterHawk
Select your team to see how ClusterHawk fits into your workflow
Challenge
Alert fatigue - hundreds of IPs from SIEM alerts with no time to investigate each manually. Need to separate noise from real threats.
Workflow
Upload suspicious IPs from SIEM alerts or threat feeds
ClusterHawk groups them by infrastructure characteristics
Review cluster quality, features, and anomaly indicators
Prioritize and label clusters (e.g., "Suspicious C2")
Reduce triage time from hours to minutes. Scattered IPs become actionable clusters with quality scores and labels that track investigation context.