enhancement Security Settings : Clustering Algorithm

[jaynomyaro]

Summary

Enhance the Security Settings module by introducing a clustering-based analysis engine that groups users, devices, sessions, and security events according to behavioral patterns and risk characteristics. This enables more intelligent threat detection, anomaly identification, and security recommendations.

Changes
Implement clustering algorithm for security behavior analysis
Group authentication events based on similarity metrics
Cluster devices, login locations, and session activity patterns
Add risk classification for clustered security events
Generate security insights from identified clusters
Introduce configurable clustering parameters in Security Settings
Add support for periodic cluster recalculation
Store cluster metadata for reporting and auditing
Key Features
Behavioral Clustering
Login activity clustering
Device usage pattern grouping
Session behavior analysis
Geographic access pattern clustering
Authentication frequency analysis
Risk Detection
Identify anomalous login behavior
Detect unusual device activity
Flag suspicious location changes
Highlight outlier security events
Assign risk levels to detected clusters
Security Recommendations
Suggest additional verification requirements
Recommend password resets for high-risk activity
Trigger security alerts for abnormal behavior
Surface actionable security insights to administrators
Configuration Options
Adjustable clustering sensitivity
Configurable similarity thresholds
Risk score weighting controls
Cluster refresh intervals
Retention settings for cluster history
Performance Improvements
Efficient clustering computation for large datasets
Incremental cluster updates where applicable
Optimized storage of cluster metadata
Reduced processing overhead through batching
Testing
Add unit tests for clustering logic
Add tests for similarity calculations
Verify correct cluster formation across datasets
Test anomaly and outlier detection behavior
Add integration tests for security event processing
Validate risk score generation and recommendations
Add performance tests for large-scale event datasets
Security Considerations
Ensure clustering operates on authorized data only
Protect sensitive user information during analysis
Maintain auditability of generated risk assessments
Prevent unauthorized access to cluster insights
Motivation

Traditional rule-based security checks may miss subtle patterns across large datasets. Clustering enables the system to identify related behaviors, surface anomalies more effectively, and provide smarter security recommendations, improving overall account and platform protection.

Checklist
Implemented clustering engine for security analysis
Added configurable clustering parameters
Added anomaly and risk detection capabilities
Added cluster-based security recommendations
Added unit, integration, and performance tests
Added audit and reporting support
Updated security settings documentation
Verified scalability and accuracy across representative datasets..closed #394

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[jaynomyaro]

Done ma,can I get more issues from you,I enjoy working with you ma'am,thanks @RUKAYAT-CODER

[RUKAYAT-CODER]

Kindly fix workflow(lint, type check)
sure you can get more issues