9152360519

admin@brmodakanalytics.com

Intelligent Threat Progression Monitor

Powered By HIDDEN MARKOV MODELS

We are authorized distributors of cybersecurity solutions licensed by Bayesian Cybersecurity.

Alert Volume Optimizer - Transform 45,000+ daily security alerts into 950 actionable threat intelligence reports with 97.9% noise reduction while preserving complete attack visibility.

Explore Capabilities View Performance
97.9%
Noise Reduction
47:1
Compression Ratio
89.5%
Time Saved
76.9%
Avg. Confidence

45,000+

Daily Alerts

Raw Security Events

HMM Processing

950

Threat Sessions

Actionable Intelligence

The Security Operations Challenge

Financial services face overwhelming security alert volumes that bury real threats

Current Reality in Financial Services

  • 45,000+ daily alerts overwhelm security teams
  • 95% false positives bury real threats in noise
  • Entire shifts wasted on alert triage instead of threat hunting
  • Detection delays as critical attacks hide among thousands
  • Regulatory pressure for enhanced cybersecurity monitoring

BR Modak Innovation

Mathematical Approach

Transform 45,000 raw alerts into 950 contextual threat intelligence reports using Hidden Markov Models and Bayesian probability scoring.

  • 97.9% noise reduction
  • Complete attack visibility preserved
  • Real-time processing capabilities

Mathematical Foundation

Hidden Markov Models for Cybersecurity Intelligence

Attack Sequence Learning

Models attack progressions through phases: Normal Operations → Reconnaissance → Initial Access → Lateral Movement → Objective Execution

  • Probabilistic transitions between states
  • Real-world data learning
Session-Based Correlation

Groups related alerts into coherent attack narratives with 47:1 compression ratio while maintaining full attack context.

  • 45,000 → 950 sessions
  • Complete context preserved
Bayesian Probability Scoring

Provides probabilistic threat confidence (0–100%) with intervals for risk-based investigation prioritization.

  • Mathematical threat ranking
  • Risk-based prioritization

Prototype Performance Results

Real data processing capabilities with Microsoft Defender integration

Volume Intelligence

44,933
Input Alerts
949
Threat Sessions
  • Compression Ratio: 47:1 (97.9% reduction)
  • Context preserved with real-time processing
  • Microsoft Defender full integration

Threat Intelligence Distribution

High-Risk Sessions (>70%) 618 (65.1%)
Medium-Risk (30–70%) 276 (29.1%)
Low-Risk (<30%) 55 (5.8%)
Average Threat Confidence: 76.9%

Advanced Detection Capabilities

Beyond traditional rule-based systems with sophisticated threat detection

Multi-stage Attack Correlation

Tracks attack progressions across multiple phases with mathematical precision and behavioral anomaly modeling.

Zero-day Recognition

Detects unknown threats without signatures using behavioral analysis and "living off the land" abuse detection.

Real-time Progression Tracking

Monitors attack progression in real-time with MITRE ATT&CK classification and mathematical threat prioritization.

Full Forensic Timeline

Provides complete forensic timeline per attack with detailed session-level attack narratives and evidence chains.

Real Prototype Session Example

Session ID 445 - Lateral Movement Attack
HMM Path: Normal → Recon → Lateral Movement
Probability: 89.2%
Alerts Correlated: 52 events
Certainty: 94.7%
Key Attack Events Detected:
  • Internal network scanning
  • Suspicious PowerShell usage
  • Credential theft via LSASS
  • SMB-based lateral movement
Recommended Action

Immediate priority review required - High confidence lateral movement detected

Technical Architecture & Current Capabilities

Platform overview and integration capabilities

Current Integration
  • Microsoft Defender (full API integration)
  • JSON alert parsing & feature extraction
  • Hidden Markov Model + Bayesian inference
  • Structured threat intelligence output
Technical Stack
  • • Python + PyTorch HMM engine
  • • Real-time ingestion via REST APIs
  • • Containerized microservices
  • • Scalable: 100,000+ alerts/hour
Planned Roadmap
SIEM
  • • Splunk
  • • QRadar
  • • ArcSight
EDR
  • • CrowdStrike
  • • SentinelOne
Network
  • • Palo Alto
  • • Fortinet
Cloud
  • • Azure Sentinel
  • • AWS GuardDuty

Current Status: Advanced prototype with full Microsoft Defender integration

Regulatory Awareness & Financial Services Focus

Security intelligence for regulated environments

Regulatory Considerations
  • SEBI: Continuous monitoring emphasis
  • Audit Trails: Mathematical documentation
  • Faster Response: Fewer false positives
  • Risk Quantification: Probabilistic scoring
Financial Sector Features
  • PII Detection Patterns
  • Transaction Anomaly Detection
  • Insider Threat via Behavior Deviation
  • Third-Party/Supply Chain Risk

Compliance Benefits

Audit-Ready Reports
Auto-Documentation
Timely Detection Metrics
Risk Scoring for Governance
Regulatory Value

Transforms compliance from reactive to proactive threat intelligence

Competitive Technical Advantages

How our approach differs from traditional cybersecurity solutions

vs. Traditional SIEM

Feature BR Modak Traditional SIEM
Analysis Method Probabilistic learning Static rules
Alert Processing Session-based grouping Alert-by-alert analysis
False Positives 2.1% 95%
Model Adaptation Self-adapting models Manual tuning

vs. AI/ML Tools

  • Interpretable HMM logic vs Black-box models
  • Attack-specific math models vs Generic training
  • Clear progression insights vs Poor explainability
  • Dynamic Bayesian updating vs Static deployment
  • Open framework vs Vendor dependency

Key Differentiators

Strong Mathematical Core
Narrative Coherence
Temporal Threat Tracking
Enterprise-scale Performance

Current Development Status

Advanced prototype with validated performance metrics

Prototype Achievements
  • Microsoft Defender live API integration
  • HMM trained/tested on 44,933 alerts
  • 97.9% alert volume reduction achieved
  • Sub-second correlation performance
  • Numerically stable for production
Next Development Phase
  • Multi-source ingestion (SIEM, EDR, Network)
  • Interactive web dashboard UX
  • Automated response integration
  • Self-learning model improvements

Partnership Opportunities

We're seeking partnerships with financial institutions for pilot deployments and validation.

Pilot Deployments
For validation
Early Access
Tech consultation
Anonymized Data
For training
Feature Co-development
Custom requirements

Our Open Source Contributions

Extensive cybersecurity contributions used by hundreds of global organizations

Bayesian Traffic Prism

Pixel-based cybersecurity solution to score and terminate sessions in real-time from multiple attack scenarios. Actively considered by global military organizations.

View on GitHub
Bayesian Second Opinion

Analyzes security logs by finding similar historical incidents and attack patterns, then uses an LLM to provide contextualized threat analysis.

View on GitHub
Custom LLM WAF

Creates dynamic session ID and uses custom LLMs for zero-day detection. Implemented as nginx plugin making it applicable for 99%+ critical infrastructure.

View on GitHub

Global Impact

Our open source cybersecurity solutions are used by hundreds of global organizations in various formats, with many executives following our developments. We're committed to being a positive force in the global cybersecurity domain.

100+
Organizations
50+
Countries
10K+
Downloads

Ready to Transform Your Security Operations?

Experience the power of mathematical threat intelligence with our advanced prototype

admin@brmodakanalytics.com Request Demo
Advanced Prototype

Full Microsoft Defender integration with proven results

Partnership Ready

Available for pilot deployments with financial institutions

Azure Marketplace

Coming soon through BR Modak Analytics